Knowledge based performance management system

ABSTRACT

An automated system ( 100 ) and method for knowledge based performance management for an organization. After extracting data from existing narrowly focused systems, mission measures and organization levels are defined for one or more organizations. The elements, factors and risks that contribute to mission measure performance by organization level and organization are systematically defined and stored in a ContextBase using up to six context layers. ContextBase information is extracted for specified combinations of context layers, organization levels and organizations as required to produce complete context frames. The complete context frames are then used by a series of applications for reviewing, analyzing, forecasting, planning and optimizing organization performance.

CROSS REFERENCE TO RELATED APPLICATIONS AND PATENTS

The subject matter of this application is related to the subject matterof: application Ser. Nos. 09/295,337 filed Apr. 21, 1999 (nowabandoned), 09/421,553 filed Oct. 20, 1999 (now abandoned), 09/775,561filed Feb. 5, 2001 (now abandoned), application Ser. No. 09/678,109filed Oct. 4, 2000, application Ser. No. 09/938,555 filed Aug. 27, 2001(now abandoned), application Ser. No. 09/994,720 filed Nov. 28, 2001,application Ser. No. 09/994,739 filed Nov. 28, 2001, application Ser.No. 10/046,316 filed Jan. 16, 2002, application Ser. No. 10/012,375filed Dec. 12, 2001, application Ser. No. 10/025,794 filed Dec. 26,2001, application Ser. No. 10/036,522 filed Jan. 7, 2002, applicationSer. No. 10/124,240 filed Apr. 18, 2002, application Ser. No. 10/166,758filed Jun. 12, 2002, U.S. Pat. No. 5,615,109 “Method of and System forGenerating Feasible, Profit Maximizing Requisition Sets, U.S. Pat. No.6,321,205 “Method of and System for Modeling and Analyzing BusinessImprovement Programs” and U.S. Pat. No. 6,393,406 “Method and System forBusiness Valuation” by Jeff S. Eder, the disclosure of which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

This invention relates to a computer based method of and system forknowledge based performance management for any organization with one ormore quantifiable mission measures.

Leaders in business, industry and government have collectively investedbillions of dollars in a wide variety of software systems over the lasttwenty years. This enormous investment was initially focused onautomating core processes such as order management, payroll, procurementand production. In the last several years a wide variety of analyticalapplications have been developed to supplement the capabilities of theincreasingly complex systems and suites that manage core processes. As aresult, the size and cost of the information technology infrastructurein the average organization has increased dramatically over the lasttwenty years. Information technology (IT) is now the second largestcost—after personnel—in many corporations and the average Fortune 500firm now uses over thirty separate software systems to manageperformance. In the global marketplace there are over seventy differenttypes of enterprise software systems being offered to manage a narrowslice of organization performance (hereinafter, will be referred to asnarrow systems).

The good news is that many of these separate, narrow systems provideuseful information and have become integral parts of the organizationsthat they support. The bad news is that these independent, narrowsystems utilize a wide variety of languages, platforms and technologies.This complexity has made it challenging and expensive to integrate thesesystems. As a result, the number of systems that are being integrated islimited. The limited integration of disparate, independent systems hasseveral negative impacts on the effectiveness of the overall ITinfrastructure including:

-   -   the ability to flexibly respond to constantly evolving business        requirements is restricted;    -   the ability to partner with other companies to improve quality        and efficiency is limited;    -   the ability to analyze and manage business performance is        constrained by the functional “silos” defined by the different        systems; and    -   the ability to transition to an Internet-centric operating mode        is compromised.

Many feel that these limitations are already responsible for the poorfinancial returns a number of organizations have received from their ITinvestments. A recent article in an industry trade magazine echoed thissentiment when noting that “most businesses are home to scores ofinformation systems that remain uselessly disconnected from one another.Until those systems are integrated, technology investments won't live upto expectations.” The sheer complexity of managing and maintaining thislarge number of systems is another factor contributing to poor financialreturns for many companies that have installed these systems. Anotherfactor contributing to the poor financial returns is the fact that theexplosive growth in the size and complexity of their informationtechnology systems has surpassed the ability of many companies toorganize and apply the data generated by these same systems. The captionfrom a recent cartoon in the New Yorker summarized the situation of many“we have lots of information technology . . . we just don't have anyinformation.”

Unfortunately, the New Yorker cartoon reflects the reality in mostorganizations that have mountains of digital content and data thatcannot be located, accessed or used. These mountains of data and contentcontain nuggets of knowledge that have high creation costs and highvalue. To date there has been no economical way to catalog suchinformation or provide effective ways to search, identify, retrieve anduse the valuable knowledge and business data available on the world wideweb and hidden in the hard drives, databases, datamarts anddata-warehouses of corporations and government agencies around theglobe. Examples of the enormous waste caused by not being able toidentify and retrieve the right information at the right time arelegion. The most visible example is obviously the Sep. 11^(th) disaster.Many feel that if only the “FBI knew what the FBI knew” about flighttraining in Phoenix and a suspected terrorist in Minnesota, then theentire disaster could have been avoided with the consequent savings ofhuman life, suffering and property damage. The FBI is not the onlyorganization having a problem identifying and applying all theinformation contained in its systems. For example, Hewlett Packardrecognizes that its knowledge about markets, products and customers isits biggest source of competitive advantage. But because the firm ishighly decentralized, its knowledge is dispersed across business unitsthat have little perceived need to share with one another. Many largehospitals and HMO's also suffer from the same sort of diffusion ofexpertise and knowledge throughout the organization. All too often, acrisis is solved before the expert can be located. A method fororganizing and applying the knowledge that is already available wouldclearly help this alleviate this problem. Many feel that businessprocess integration will be a cure all for many of these ills. Whilebusiness process integration is of some help, it is not a panaceabecause it only replaces the vertical “functional silos” withhorizontal, “process silos”.

Improving our ability to develop and apply knowledge will do more thanprevent disasters and ease crises it will also help improve performanceand create value. Value will be created in several ways. First, thelimitations on IT infrastructure effectiveness described previously willbe reduced or eliminated. Second, support for the new collaborativeapproach to projects and work that increasingly pervades the moderneconomy will be increased if knowledge can be more readily shared. Thehead of research from a large pharmaceutical company recently noted that“the creation of value is coming increasingly from the collaboration ofgroups . . . the point is no longer to manage the silos, but to bringtogether around a problem the right group of people with the rightknowledge.” The increasing amount of partnerships that are being formedbetween different companies is another force that will increase thevalue impact of providing a more systematic, method for developing,storing and sharing knowledge.

From the preceding discussion, it is clear that in an era of dataoverload and increasing collaboration, we need a new approach to get theright information to the right person and/or to the right system at theright time. As discussed later, once a system is in place to get theright information to the right person and/or system at the right timenew systems to process the information will also be needed. Fortunately,these new systems will also reduce the complexity associated with usinginformation technology systems to manage organization performance by anorder of magnitude.

A critical first step in defining a new approach to solving the problemof “getting the right knowledge to the right place” is to clearly definethe terms: data, information, context and knowledge. Data is anythingthat is recorded. This includes records saved in a digital format anddata stored using other means. A subset of the digital data isstructured data such as transaction data and data stored in a databasefor automated retrieval. Data that is not structured is unstructureddata. Unstructured data includes data stored in a digital format anddata stored in some other format (i.e. paper, microfilm, etc.).Information is data plus context of unknown completeness. Knowledge isdata plus complete context. Complete context is defined as: all theinformation relevant to the decision being made using the data at aspecific time. If a decision maker has data and the complete context,then providing additional data or information that is available at thetime the decision is being made will not change the decision that wasmade. If additional data or information changes the decision, then thedecision maker had “partial context”.

We will use an example to illustrate the difference between data,partial context, complete context and knowledge. The example is shown inTable 1 and Table 2.

TABLE 1 Data: We received a check for $6,000 from Acme Tool today.Partial Context: Acme Tool owed our division $36,000 and promised to paythe entire balance due last week. We are due to ship them another 100widgets next Tuesday, since we have only 50 in the warehouse we need tostart production by Friday if we are going to meet the promised date.Decision based on data + partial context: Stop production and havecustomer service put a credit hold flag on their account, then havesomeone call them to find out what their problem is.

TABLE 2 Data: We received a check for $6,000 from Acme Tool today.Complete context: Acme Tool owed our division $36,000 and promised topay the entire balance due last week. We are due to ship them another100 widgets next Tuesday, since we have only 50 in the warehouse we needto start production by Friday if we are going to meet the promised date.Acme is a key supplier for Project X in the international division. Theinternational division owes Acme over $75,000. They expected to pay Acmelast week but they are late in paying because they have had someproblems with their new e.r.p. system. Netting it all out, ourorganization actually owes Acme $45,000. We have also learned that ourbiggest competitor has been trying to get Acme to support their effortsto develop a product like Project X. Decision based on knowledge (data +complete context): See if there is anything you can do to expedite thewidget shipment. Call Acme, thank them for the payment and see if theyare OK with us deducting the money they owe us from the money thematerials division owes them. If Acme OKs it, then call theinternational division and ask them to do the paperwork to transfer themoney to us so we can close this out.The example in Tables 1 and 2 illustrates that there is a cleardifference between having data with partial context and havingknowledge. Data with partial context leads to one decision while datawith complete context creates knowledge and leads to another completelydifferent decision. The example also suggests another reason (inaddition to not being able to find anything) that so many firms are notrealizing the return they expect from their investments in narrowperformance management systems. Virtually every information technologysystem being sold today processes and analyzes data within the narrowsilo defined by the portion of the enterprise it supports. As a result,these systems can not provide the complete context required to turn datainto knowledge. Recently announced products for federated data analysisdo not fully address this problem because they are not capable ofdeveloping and/or processing all the types of information required toproduce a complete context analysis.

Another limitation of all known performance management systems is theircomplete reliance on structured historical data. The problem with thisis that not all data are stored and that most of the data that is storedis stored in an unstructured format that is difficult to process. Themost common estimate is that 80% of the data that is stored digitally isstored in an unstructured format. A number of products are beingdeveloped to help structure unstructured digital data. The system of thepresent invention is capable of accepting input from these systems. Thesystem of the present invention also has the ability to structure andprocess unstructured: text data, video data, geo-coded data and web dataon its own. This leaves the problem of data that has not been stored inany system as an area needing further development. While much of thedata that has not been stored may not be useful for performancemanagement, the data that resides with subject-matter experts ispotentially very valuable. In fact, as the world moves into anincreasingly uncertain environment with a growing number ofnon-traditional threats and increasingly volatile weather patterns, theneed to rely on information from subject-matter experts is expected toincrease dramatically.

A method for systematically incorporating data from subject-matterexperts into knowledge based systems is clearly needed. However, to besuccessful, this method needs to overcome a few potential problems.While subject-matter experts have a great deal of knowledge about aparticular field, it is more likely than not that:

-   -   1. they do not have any expertise in knowledge representation,        and    -   2. they do not have any expertise in probability theory.        As a result, the subject-matter experts may have difficulty        communicating their expertise in a manner that can be readily        processed by a data fusion analysis. While overcoming both        problems is important, solving the second problem is        particularly important because subject-matter experts        involvement is most likely to be critical in developing        assessments for the increasing number of situations that have        little or no precedent, very limited data and a consequent high        degree of uncertainty.

In light of the preceding discussion, it is clear that it would bedesirable to develop methods and systems that could define the completecontext required for effectively managing performance. The system shouldsupport individuals working alone, individuals working in teams, teamsworking independently, teams working together, organizations workingalone and organizations that are collaborating with other organizations.Ideally, this system would be capable of reducing IT infrastructurecomplexity while sifting through all the available data andincorporating newly created data as required to define the full contextfor performance related analysis and decision making. In short, the newmethods and systems should help organizations improve their performanceby developing, storing, retrieving and applying complete contextknowledge in an automated fashion.

SUMMARY OF THE INVENTION

It is a general object of the present invention to provide a novel,useful system that develops, analyzes, stores and applies completecontext information for use in improving the performance of anyorganization combination, organization or subset of an organization witha quantifiable mission. For simplicity, we will refer to the collectionof different subsets of an organization that can be supported by thesystem for knowledge based performance management as organizationlevels. This new system overcomes the limitations and drawbacks of theprior art that were described previously.

Processing in the Knowledge Based Performance Management System iscompleted in three steps: The first step in the novel method forknowledge based performance management involves using data provided byexisting narrow systems and the nine key terms described previously todefine mission measures for each organization level. As part of thisprocessing data from the world wide web. unstructured data, geo-codeddata, and video data are processed and made available for analysis. Theautomated indexation, extraction, aggregation and analysis of data fromthe existing, narrow computer-based systems significantly increases thescale and scope of the analyses that can be completed by users. Thisinnovation also promises to significantly extend the life of the narrowsystems that would otherwise become obsolete. The system of the presentinvention is capable of processing data from the “narrow” systems listedin Table 3.

TABLE 3  1. Accounting systems;  2. Alliance management systems;  3.Asset management systems;  4. Brand management systems;  5.Budgeting/financial planning systems;  6. Business intelligence systems; 7. Call management systems;  8. Cash management systems;  9. Channelmanagement systems; 10. Commodity risk management systems; 11. Contentmanagement systems; 12. Contract management systems; 13. Credit-riskmanagement system 14. Customer relationship management systems; 15. Dataintegration systems; 16. Demand chain systems; 17. Decision supportsystems; 18. Document management systems; 19. Email management systems;20. Employee relationship management systems; 21. Energy risk managementsystems; 22. Executive dashboard systems; 23. Expense report processingsystems; 24. Fleet management systems; 25. Fraud management systems; 26.Freight management systems; 27. Human capital management systems; 28.Human resource management systems; 29. Incentive management systems; 30.Innovation management systems; 31. Insurance management systems; 32.Intellectual property management systems; 33. Intelligent storagesystems 34. Interest rate risk management systems; 35. Investorrelationship management systems; 36. Knowledge management systems; 37.Learning management systems; 38. Location management systems; 39.Maintenance management systems; 40. Material requirement planningsystems; 41. Metrics creation system 42. Online analytical processingsystems; 43. Ontology management systems; 44. Partner relationshipmanagement systems; 45. Payroll systems; 46. Performance managementsystems; (for IT assets) 47. Price optimization systems; 48. Privateexchanges 49. Process management systems; 50. Product life-cyclemanagement systems; 51. Project management systems; 52. Projectportfolio management systems; 53. Revenue management systems; 54. Riskmanagement information system 55. Risk simulation systems; 56. Salesforce automation systems; 57. Scorecard systems; 58. Sensor gridsystems; 59. Service management systems; 60. Six-sigma qualitymanagement systems; 61. Strategic planning systems; 62. Supply chainsystems; 63. Supplier relationship management systems; 64. Support chainsystems; 65. Taxonomy development systems; 66. Technology chain systems;67. Unstructured data management systems; 68. Visitor (web site)relationship management systems; 69. Weather risk management systems;70. Workforce management systems; and 71. Yield management systems

The quantitative mission measures that are initially created using theextracted narrow system data from each organization can take any form(please note: a new organization could use the Knowledge BasedPerformance Management System to generate the information required tocreate mission measures without the use of narrow system data). For manyof the lower organization levels (combinations being the highest leveland an element being the lowest organization level) the mission measuresare simple statistics like percentage achieving a certain score, averagetime to completion and the ratio of successful applicants versusfailures. At higher levels more complicated mission measures aregenerally used. For example, Table 5 shows a three part mission measurefor a medical organization mission—patient health, patient longevity andfinancial break even. As discussed in the cross-referenced patentapplication Ser. Nos. 10/071,164 filed Feb. 7, 2002; 10/124,240 filedApr. 18, 2002 and 10/124,327 filed Apr. 18, 2002, commercial businessesthat are publicly traded generally require five risk adjusted measuresper enterprise—a current operation measure, a real option measure, aninvestment measure, a derivatives measure and a market sentimentmeasure. The system of the present invention will support the use ofeach of the five measures described in the cross referenced patentapplications in an automated fashion. Also, as described in thecross-referenced patent application Ser. Nos. (10/124,240 filed Apr. 18,2002 and 10/124,327 filed Apr. 18, 2002) the total risk associated withthese five measures equals the risk associated with equity in theorganization. The Knowledge Based Performance Management System willalso support the automated definition of other mission measuresincluding: each of the different types of event risks alone or incombination, each of the different types of factor risks alone or incombination, cash flow return on investment, accounting profit, andeconomic profit.

The system of the present invention provides several other importantadvances over the systems described in these cross-referencedapplications, including:

-   -   1. the same performance management system can be used to manage        performance for all organization levels;    -   2. the user is free to specify more than five mission measures        for every organization level;    -   3. the user can assign a weighting to each of the different        mission measures which is different than the risk adjusted value        measure; and    -   4. the user is free to specify mission measures that are        different than the ones described in the prior cross-referenced        patent applications.

After the user defines the mission measures and the data available forprocessing is identified, processing advances to second stage ofprocessing where mission-oriented context layers for each organizationlevel are developed and stored in a ContextBase (60). In the finalprocessing step the context layers and organization levels are combinedas required to develop context frames for use in analyzing, forecasting,planning, reviewing and/or optimizing performance using CompleteContext™ Systems (601, 602, 603, 604, 605, 606, 607 and 608) and closingthe loop with any remaining narrow systems as required to supportKnowledge Based Performance Management.

The system of the present invention is the first known system with theability to systematically develop the context required to support thecomprehensive analysis of mission performance and turn data intoknowledge. Before completing the summary of system processing, we willprovide more background regarding mission-oriented context, contextlayers and the Complete Context™ Systems (601, 602, 603, 604, 605, 606,607 and 608).

The complete context for evaluating a mission performance situation cancontain up to six distinct types of information:

-   -   1. Information that defines the physical context, i.e. we have        50 good widgets in the warehouse available for shipment. If we        need to make more, we need to use the automated lathe and we        need to start production 2 days before we need to ship;    -   2. Information that defines the tactical (aka administrative)        context, i.e. we need to ship 100 widgets to Acme by Tuesday;    -   3. Information that defines the instant impact, i.e. Acme owes        us $30,000 and the price per widget is $100 and the cost of        manufacturing widgets is $80 so we make $20 profit per unit (for        most businesses this could be defined as the short term economic        context).    -   4. Information that defines the organizational context, i.e.        Acme is also a key supplier for the new product line, Project X,        that is expected to double our revenue over the next five years;    -   5. Information that defines the mission impact, i.e. Acme is one        of our most valuable customers and they are a key supplier to        the international division, and    -   6. Information that defines the social environment, i.e. our        biggest competitor is trying to form a relationship with Acme.        We will refer to each different type of information as a context        layer. Different combinations of context layers from different        organization levels and/or organizations are relevant to        different decisions. Each different combination of context        layers, organization levels and organizations is called a        context frame.

The ability to rapidly create context frames can be used to rapidlyanalyze a number of different operating scenarios including an alliancewith another organization or a joint exercise between two organizations.For example, combined context frames could be created to support theArmy and the Air Force in analyzing the short and long term implicationsof a joint exercise as shown in Table 4. It is worth noting at thispoint that the development of a combination frame is most effective whenthe two organizations share the same mission measures.

TABLE 4 Context Frame From These Description Combines These LayersOrganizations JV short term Physical, Tactical & Instant Army and AirForce JV strategic Physical, Tactical, Instant, Army and Air ForceOrganization, Mission & Social EnvironmentUsing the context frames from the combined organizations to guide bothtactical (short-term) and strategic analysis and decision making wouldallow each organization to develop plans for achieving a common goalfrom the same perspective (or context) while still maintainingindependence. This capability provides a distinct advantage overtraditional analytical applications that generally only consider thefirst three layers of context when optimizing resource allocations. Intaking this approach, traditional analytic systems analyze and optimizethe instant (short-term) impact given the physical status and thetactical situation. Because these systems generally ignore organization,mission and environmental contexts (and some aspects of instant impact),the recommendations they make are often at odds with common sensedecisions made by line managers that have a more complete context forevaluating the same data. This deficiency is one reason many have notedthat “there is no intelligence in business intelligence applications”.

Before moving on to better define context, it is important tore-emphasize the fact that the six layers of context we have defined canalso be used to support performance management, analysis and decisionmaking in areas other than commercial business. In fact, the system ofthe present invention will measure and help manage performance for anyorganization or group with a quantifiable mission. For example, Table 5illustrates the use of the six layers in analyzing a sample businesscontext and a sample medical context.

TABLE 5 Business Medical (patient health & longevity, (shareholder valuemaximization mission) financial break even missions) Social Environment:competitor is trying to Social Environment: malpractice form arelationship with Acme insurance is increasingly costly Mission: Acme isa valuable customer and a Mission: treatment in first week key supplier,relationship damage will improves 5 year survival 18%, 5 year decreasereturns and increase risk reoccurrence rate is 7% higher for procedure AOrganization: Acme supports project X in Organization: Dr. X has acommitment to international division assist on another procedure MondayInstant: we will receive $20 profit per unit Instant: survival rate is99% for procedure A and 98% for procedure B Tactical: need 100 widgetsby Tuesday for Tactical: patient should be treated next Acme, need tostart production Friday week, his insurance will cover operationPhysical: 50 widgets in inventory, automated Physical: operating room Ahas the lathe is available Friday right equipment and is availableMonday, Dr. X could be available MondayOur next step in completing the background information is to define eachcontext layer in more detail. Before we can do this we need to definenine key terms: mission, element, resource, asset, agent, action,commitment, priority and factor, that we will use in the defining thelayers.

-   -   1. Mission—purpose of organization translated into one or more        mission measures—examples: market value, patient survival rate,        and production efficiency;    -   2. Element—something of value (note value may be negative) that        is related to an organization—examples: property, relationships        and knowledge;    -   3. Resource—subset of elements that are routinely transferred to        others and/or consumed—examples: raw materials, products,        employee time and risks;    -   4. Asset—subset of elements that support the consumption,        production or transfer of resources. They are generally not        transferred to others and/or consumed—examples: brands, customer        relationships; and equipment;    -   5. Agent—subset of elements that can participate in an        action—examples: customers, suppliers, salespeople.    -   6. Action—consumption, production, acquisition or transfer of        resources that support organization mission—examples: sale of        products and development of a new product (actions are a subset        of events which include anything that is recorded);    -   7. Commitment—an obligation to perform an action in the        future—example: contract for future sale of products;    -   8. Priority—relative importance assigned to actions and mission        measures; and    -   9. Factor—conditions external to organization that have an        impact on organization performance—examples: commodity prices,        weather, earnings expectation.        In some cases agent, element and/or action classes may be        defined by an industry organization (such as the ACORD        consortium for insurance). If this is the case, then the        pre-defined classes are used as a starting point for key term        definition. In any event, we will use the nine key terms to        define the six context layers shown below.    -   1. Physical context—information about the physical status,        location and performance characteristics of elements;    -   2. Tactical context—information about completed actions, action        procedures, action priorities, commitments and events;    -   3. Instant context—information about the short-term impact of        actions, the short term impact of events and the expected impact        of commitments;    -   4. Organization context—information about the inter-relationship        between factors, elements and/or actions (includes process maps        and may be action specific);    -   5. Mission context—information about the impact of elements,        factors and actions on mission measures (may be agent specific)        and mission measure priorities; and    -   6. Social Environment context—information about factors in the        social environment in which the organization is completing        actions (includes market dynamics).        Management can establish alert levels for data within each        layer. Management control is defined and applied at the tactical        and mission levels by assigning priorities to actions and        mission measures. Using this approach the system of the present        invention has the ability to analyze and optimize performance        using management priorities, historical measures or some        combination of the two. It is worth noting at this point that        the layers may be combined for ease of use, to facilitate        processing or as organizational requirements dictate. We will        refer to the first three layers (physical, tactical and instant)        as the administrative layers and the last three layers        (organization, mission and social environment) as the strategic        layers (aka strategic business context layers).

As discussed previously, analytical applications are generally concernedwith only the first three (3) context layers (physical, tactical andinstant). One reason for this is that the information to define the lastthree layers of context (organization, mission and socialenvironment—the strategic context layers) are not readily available andmust be developed. The Knowledge Based Performance Management System(100) develops context in a manner similar to that described previouslyin cross referenced application Ser. Nos. 10/071,164 filed Feb. 7, 2002;10/124,240 filed Apr. 18, 2002 and 10/124,327 filed Apr. 18, 2002. Inthe preferred embodiment, the Knowledge Based Performance ManagementSystem works in tandem with a Business Process Integration Platform tointegrate narrow systems into a complete system for performancemanagement as shown in FIG. 13. However, in an alternate mode the systemwould provide the functionality for process integration in theorganization tier of the software architecture. In either mode, thesystem of the present invention supports the development of thestrategic context layers and the storage of all six context layers asrequired to create a mission-oriented ContextBase (60).

The creation of the mission-oriented ContextBase (60) provides severalimportant benefits. One of the key benefits the mission-orientedContextBase (60) provides is that it allows the system of the presentinvention to displace the seventy plus narrow systems with sevenComplete Context™ Systems (601, 602, 603, 604, 605, 606 and 607) thatprovide more comprehensive analytical and management capabilities. Theseven Complete Context™ Systems (601, 602, 603, 604, 605, 606 and 607)are briefly described below:

-   -   1. Complete Context™ Analysis System (602)—analyzes the impact        of specified changes on a specific context frame. Software to        complete these analyses can reside on the application server        with user access through a browser, it can reside in an applet        that is activated as required or it can reside on a client        computer with the context frame being provided by the Knowledge        Based Performance Management System as required. Context frame        information may be supplemented by simulations and information        from subject matter experts as appropriate. Cross referenced        U.S. patent application Ser. No. 10/025,794 describes a similar        client-side application for asset and process analysis. Cross        referenced U.S. patent application Ser. No. 10/036,522 describes        a similar client-side application for risk analysis. Cross        referenced U.S. patent application Ser. No. 10/166,758 describes        a similar client-side application for purchasing analysis. Cross        referenced application Ser. Nos. 10/046,316 and 10/124,240        describe a server based system for analyzing a multi-enterprise        organization.    -   2. Complete Context™ Forecast System (603)—forecasts the value        of specified variable(s) using data from all relevant context        layers. Completes a tournament of forecasts for specified        variables and defaults to a multivalent combination of forecasts        from the tournament using methods similar to those first        described in U.S. Pat. No. 5,615,109. Software to complete these        forecasts can reside on the application server with user access        through a browser, it can reside in an applet that is activated        as required or it can reside on a client computer.    -   3. Complete Context™ Optimization System (604)—simulates        organization performance and identifies the optimal mode for        operating a specific context frame. If there is more than one        mission measure, the optimization system can use management        input or the relative levels or relevance found historically to        weight the different measures. Software to complete these        simulations and optimizations can reside on the application        server with user access through a browser, it can reside in an        applet that is activated as required or it can reside on a        client computer with the context frame being provided by the        Knowledge Based Performance Management System as required. Cross        referenced U.S. patent application Ser. No. 10/025,794 describes        a similar client-side application for asset and process        optimization. Cross referenced U.S. patent application Ser. No.        10/036,522 describes a similar client-side application for risk        optimization. Cross referenced U.S. patent application Ser. No.        10/166,758 describes a similar client-side application for        purchasing optimization. Cross referenced application Ser. Nos.        10/046,316 and 10/124,240 describe a similar server based system        for optimizing a multi-enterprise organization.    -   4. Complete Context™ Planning System (605)—system that        management uses to: establish mission measure priorities,        establish action priorities, establish expected performance        levels (aka budgets) for actions, events, instant impacts and        mission measures. These priorities and performance level        expectations are saved in the corresponding layer in the        ContextBase (60). For example, mission measure priorities are        saved in the mission layer table (175). This system also        supports collaborative planning when context frames that include        one or more partners are created. Software to complete this        planning can reside on the application server with user access        through a browser, it can reside in an applet that is activated        as required or it can reside on a client computer with the        context frame being provided by the Knowledge Based Performance        Management System as required.    -   5. Complete Context™ Project (606)—system for analyzing and        optimizing the impact of a project or a group of projects on a        context frame. Software to complete these analyses and        optimizations can reside on the application server with user        access through a browser, it can reside in an applet that is        activated as required or it can reside on a client computer with        the context frame being provided by the Knowledge Based        Performance Management System as required. Context frame        information may be supplemented by simulations and information        from subject matter experts as appropriate. Cross referenced        U.S. patent application Ser. No. 10/012,375 describes a similar        client-side application for project analysis and optimization.    -   6. Complete Context™ Review System (607)—system for reviewing        actions, elements, instant impacts and mission measures. This        system uses a rules engine to transform ContextBase (60)        historical information into standardized reports that have been        defined by different organizations. For example the Financial        Accounting Standards Board, International Accounting Standards        Board and Standard and Poors have each defined standardized        reports for reporting combinations of instant impacts, elements        and actions for commercial businesses—the income statement, the        balance sheet and the cash flow statement. Other standardized,        non-financial performance reports have been developed for        medical organizations, military operations and educational        institutions. The rules engine produces these reports on demand.        The software to complete these reports can reside on the        application server with user access through a browser, it can        reside in an applet that is activated as required or it can        reside on a client computer with the context frame being        provided by the Knowledge Based Performance Management System as        required.    -   7. Complete Context™ Transaction System (601)—system for        recording actions and commitments into the ContextBase. The        interface for this system is a browser based template that        identifies the available physical, tactical, organization and        instant impact data for inclusion in an action transaction.        After the user has recorded a transaction the system saves the        information regarding each action or commitment to the        ContextBase (60). Other applications such as Complete Context™        Analysis, Plan or Optimize can interface with this system to        generate actions or commitments in an automated fashion.        The Complete Context™ Systems (601, 602, 603, 604, 605, 606 and        607) can be supplemented by a Complete Context™ Search Engine        (608) that can help a user (20) locate relevant information        using the indices developed by layer in the ContextBase (60).        Complete Context™ Frames can also be defined for any        collaboration with another group or for any subset of the        organization including an individual, a team or a division. The        data for these frames can then be made available to the user        (20) or managers (21) on a continuous basis using a portal. Each        of the seven different systems can be flexibly bundled together        in any combination as required to complete the analysis,        planning and review required for Knowledge Based Performance        Management. For example, the systems for Complete Context™        Review (607) , Forecast (603) and Planning (605) Systems are        often bundled together. The Complete Context™ Analysis and        Optimization Systems are also bundled together in a similar        fashion.

The Complete Context™ Systems (hereinafter, referred to as the standardapplications) can replace seventy plus narrow systems currently beingused because it takes a fundamentally different approach to developingthe information required to manage performance. Narrow systems (30) tryto develop a picture of how part of the organization is performing. Theuser (20) is then left to integrate the picture. The Knowledge BasedPerformance Management System (100) develops a complete picture of howthe organization is performing, saves it in the ContextBase (60) andthen divides this picture and combines it with other pictures asrequired to provide the detailed information regarding each narrow sliceof the organization These details are included in the context framesthat are produced using information in the ContextBase (60). The contextframes are then mapped to one or more standard applications for analysisand review. Developing the complete picture first, before dividing itand recombining it as required to produce context frames, enables thesystem of the present invention to reduce IT infrastructure complexityby an order of magnitude while dramatically increasing the ability ofeach organization to manage performance. The ability to use the samesystem to manage performance for different organizational levels furthermagnifies the benefits associated with the simplification enabled by thesystem of the present invention. Because the ContextBase (60) iscontinually updated by a “learning system”, changes in organizationcontext are automatically captured and incorporated into the processingand analysis completed by the Knowledge Based Performance ManagementSystem (100).

The mission-centric focus of the ContextBase (60) provides four otherimportant benefits. First, by directly supporting mission success thesystem of the present invention guarantees that the ContextBase (60)will provide a tangible benefit to the organization. Second, the missionfocus allows the system to partition the search space into two areaswith different levels of processing. Data that is known to be relevantto the mission and data that is not thought to be relevant to mission.The system does not ignore data that is not known to be relevant,however, it is processed less intensely. Third, the processing completedin ContextBase (60) development defines a complete ontology for theorganization. As detailed later, this ontology can be flexibly matchedwith other ontologies as required to interact with other organizationsthat have organized their information using a different ontology andextract data from the semantic web in an automated fashion. Finally, thefocus on mission also ensures the longevity of the ContextBase (60) asorganization missions rarely change. For example, the primary mission ofeach branch of the military has changed very little over the last 100years while the assets, agents, resources and the social environmentsurrounding that mission have obviously changed a great deal. The samecan be said for almost every corporation of any size as almost all ofthem have a shareholder value maximization mission that has not changedfrom the day they were founded. The difference between themission-oriented approach and a more generic approach to knowledgemanagement are summarized in Table 6A.

TABLE 6A Characteristic/ Mission-oriented Generic System ContextBase(60) Knowledge Tangible benefit Built in Unknown Search SpacePartitioned by mission Un-partitioned Longevity Equal to missionlongevity Unknown

Another benefit of the novel system for knowledge based performancemanagement is that it can be used for managing the performance of anyentity with a quantifiable mission. It is most powerful when used tomanage an organization with different levels and each of these levelsare linked together as shown in the following example.

In the example, summarized in Table 6B, the Marines are interested inunderstanding what drove their mission performance in a recent conflict.

TABLE 6B Organizational hierarchy of mission performance drivers Marinesfind Division A is biggest contributor to mission performance        Division A finds Camp Pendleton         training is biggestcontributor to         mission performance                 CampPendleton identifies the                 Sergeant Mack as biggest                contributor to mission performance

As shown in Table 6B, after using the Knowledge Based PerformanceManagement System they were able to determine that Division A made thebiggest contribution to their mission measure performance. Divisions Auses the Knowledge Based Performance Management System to determine thatit was the training they received at Camp Pendleton that made thebiggest contribution to their mission measure performance. CampPendleton then uses the Knowledge Based Performance Management System toidentify Sergeant Mack as the biggest contributor to their high level oftraining mission measure performance.

Using an overall system for evaluating mission performance, each of thethree performance drivers: Division A, Camp Pendleton and Sergeant Mackwould be identified. However, because their contributions to missionperformance are closely inter-related it would be difficult to identifytheir separate contributions using an overall analysis. A better use ofthe results from an overall analysis in an environment where there is ahierarchy to performance management is to ensure that there is analignment between the mission measures at each level. For example, ifthe Camp Pendleton performance management system had identified CaptainBlack as the strongest contributor, then the Camp Pendleton system wouldclearly be out of alignment with the higher level measures thatidentified Sergeant Mack as the strongest contributor. The CampPendleton mission measures would need to be changed to bring theirperformance management system into alignment with the overall mission.Because efforts to achieve organizational alignment have reliedexclusively on management opinion and subjective measures likescorecards, some have concluded that achieving ongoing organizationalalignment is “impossible”. While it may have been impossible, theinnovative system of the present invention provides a mechanism forestablishing and maintaining alignment between different levels of ahierarchy for any organization with a quantifiable mission. It alsoprovides a separate mechanism for aligning the operation of every levelof the organization in accordance with the priorities established by themanagement team.

In addition to providing the ability to systematically analyze andimprove mission performance, the Knowledge Based Performance ManagementSystem (100) provides the ability to create robust models of the factorsthat drive action, event and instant impact levels to vary. Thiscapability is very useful in developing action plans to improve missionmeasure performance. One of the main reasons for this is that mostmission measures relate to the long term impact of actions, events andinstant impacts.

To facilitate its use as a tool for improving performance, the system ofthe present invention produces reports in formats that are graphical andhighly intuitive. By combining this capability with the previouslydescribed capabilities for: flexibly defining robust performancemeasures, ensuring organizational alignment, identifying completecontext information, reducing IT complexity and facilitating knowledgesharing, the Knowledge Based Performance Management System givesexecutives and managers the tools they need to dramatically improve theperformance of any organization with a quantifiable mission.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, features and advantages of the presentinvention will be more readily apparent from the following descriptionof the preferred embodiment of the invention in which:

FIG. 1 is a block diagram showing the major processing steps of thepresent invention;

FIG. 2 is a diagrams showing the application layer portion of softwarearchitecture of the present invention;

FIG. 3 is a diagram showing the tables in the application database (50)of the present invention that are utilized for data storage andretrieval during the processing in the innovative system for knowledgebased performance management;

FIG. 4 is a diagram showing the tables in the ContextBase (60) of thepresent invention that are utilized for data storage and retrievalduring the processing in the innovative system for knowledge basedperformance management;

FIG. 5 is a block diagram of an implementation of the present invention;

FIG. 6A, FIG. 6B, FIG. 6C and FIG. 6D are block diagrams showing thesequence of steps in the present invention used for specifying systemsettings, preparing data for processing and defining the missionmeasures;

FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D and FIG. 7E are block diagramsshowing the sequence of steps in the present invention used for creatinga mission-oriented ContextBase for by organization and organizationlevel;

FIG. 8 is a block diagram showing the sequence in steps in the presentinvention used in defining and distributing context frames and overallperformance reports;

FIG. 9 is a diagram showing the data windows that are used for receivinginformation from and transmitting information via the interface (700);

FIG. 10 is a diagram showing how the enterprise risk matrices can becombined to generate the organization matrix of risk;

FIG. 11 is a diagram showing how the enterprise performance matrices canbe combined to generate the organization performance matrix;

FIG. 12 is a sample report showing the efficient frontier forOrganization XYZ and the current position of XYZ relative to theefficient frontier;

FIG. 13 is a diagram showing how the Knowledge Based PerformanceManagement System can be integrated with a business process integrationplatform;

FIG. 14 is a block diagram shown the relationship between differentorganization levels; and

FIG. 15 is a diagram showing the format of a standard management report.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 provides an overview of the processing completed by theinnovative system for knowledge based performance management. Inaccordance with the present invention, an automated system (100) andmethod for developing a mission-oriented ContextBase (60) that containsthe six context layers for each mission measure by organization andorganization level is provided. Processing starts in this system (100)when the data extraction portion of the application software (200)extracts data from an organization narrow system database (5);optionally, a partner narrow system database (10); an external database(20); and a world wide web (25) via a network (45). Data may also beobtained from a Complete Context™ Transaction System (601) via thenetwork (45) in this stage of processing. The processing completed bythe system (100) may be influenced by a user (20) or a manager (21)through interaction with a user-interface portion of the applicationsoftware (700) that mediates the display, transmission and receipt ofall information to and from a browser software (800) such as theNetscape Navigator® or the Microsoft Internet Explorer® in an accessdevice (90) such as a phone, personal digital assistant or personalcomputer where data are entered by the user (20).

While only one database of each type (5, 10 and 20) is shown in FIG. 1,it is to be understood that the system (100) can process informationfrom all narrow systems listed in Table 3 for each organization beingsupported. In the preferred embodiment, all functioning narrow systemswithin each organization will provide data to the system (100) via thenetwork (45). It should also be understood that it is possible tocomplete a bulk extraction of data from each database (5, 10 and 20) andthe World Wide Web (25) via the network (45) using peer to peernetworking and data extraction applications. The data extracted in bulkcould be stored in a single datamart, a data warehouse or a storage areanetwork where the analysis bots in later stages of processing couldoperate on the aggregated data. A virtual database could also be usedthat would leave all data in the original databases where it could beretrieved as needed for calculations by the analysis bots over a network(45).

The operation of the system of the present invention is determined bythe options the user (20) and manager (21) specify and store in theapplication database (50) and the ContextBase (60). As shown in FIG. 3,the application database (50) contains a system settings table (140), abot date table (141) and a Thesaurus table (142).

As shown in FIG. 4, the ContextBase (60) contains tables for storingextracted information by context layer including: a mission measurestable (170), a physical layer table (171), a tactical layer table (172),an instant impact layer table (173), an organization layer table (174),a mission layer table (175), a structured data table (176), an internetlinkage table (177), a video data table (178), a social environmentlayer table (179), a text data table (180), a geo data table (181), anontology table (182), a report table (183), an element definition table(184), a factor definition table (185), an event risk table (186), ascenarios table (187), an event model table (188), an impact model table(189), a context frame table (190) and a context quotient table (191).The ContextBase (60) can exist as a datamart, data warehouse, a virtualrepository or storage area network. The system of the present inventionhas the ability to accept and store supplemental or primary datadirectly from user input, a data warehouse or other electronic files inaddition to receiving data from the databases described previously. Thesystem of the present invention also has the ability to complete thenecessary calculations without receiving data from one or more of thespecified databases. However, in the preferred embodiment all requiredinformation is obtained from the specified data sources (5, 10, 20, 601and 25) for each organization, organization level and organizationpartner.

As shown in FIG. 5, the preferred embodiment of the present invention isa computer system (100) illustratively comprised of a user-interfacepersonal computer (110) connected to an application-server personalcomputer (120) via a network (45). The application-server personalcomputer (120) is in turn connected via the network (45) to adatabase-server personal computer (130). The user interface personalcomputer (110) is also connected via the network (45) to an Internetbrowser appliance (90) that contains browser software (800) such asMicrosoft Internet Explorer or Netscape Navigator.

The database-server personal computer (130) has a read/write randomaccess memory (131), a hard drive (132) for storage of the applicationdatabase (50) and the ContextBase (60), a keyboard (133), acommunication bus (134), a display (135), a mouse (136), a CPU (137) anda printer (138).

The application-server personal computer (120) has a read/write randomaccess memory (121), a hard drive (122) for storage of thenon-user-interface portion of the enterprise section of the applicationsoftware (200, 300 and 400) of the present invention, a keyboard (123),a communication bus (124), a display (125), a mouse (126), a CPU (127)and a printer (128). While only one client personal computer is shown inFIG. 3, it is to be understood that the application-server personalcomputer (120) can be networked to fifty or more client, user-interfacepersonal computers (110) via the network (45). The application-serverpersonal computer (120) can also be networked to fifty or more server,personal computers (130) via the network (45). It is to be understoodthat the diagram of FIG. 5 is merely illustrative of one embodiment ofthe present invention as the system of the present invention couldreside in a single computer or be support by a computer grid.

The user-interface personal computer (110) has a read/write randomaccess memory (111), a hard drive (112) for storage of a clientdata-base (49) and the user-interface portion of the applicationsoftware (700), a keyboard (113), a communication bus (114), a display(115), a mouse (116), a CPU (117) and a printer (118).

The application software (200, 300 and 400) controls the performance ofthe central processing unit (127) as it completes the calculationsrequired to support knowledge based performance management. In theembodiment illustrated herein, the application software program (200,300 and 400) is written in a combination of Java and C++. Theapplication software (200, 300 and 400) can use Structured QueryLanguage (SQL) for extracting data from the databases and the World WideWeb (5, 10, 20 and 25). The user (20) and manager (21) can optionallyinteract with the user-interface portion of the application software(700) using the browser software (800) in the browser appliance (90) toprovide information to the application software (200, 300 and 400) foruse in determining which data will be extracted and transferred to theContextBase (60) by the data bots.

User input is initially saved to the client database (49) before beingtransmitted to the communication bus (124) and on to the hard drive(122) of the application-server computer via the network (45). Followingthe program instructions of the application software, the centralprocessing unit (127) accesses the extracted data and user input byretrieving it from the hard drive (122) using the random access memory(121) as computation workspace in a manner that is well known.

The computers (110, 120, 130) shown in FIG. 5 illustratively arepersonal computers or workstations that are widely available. Typicalmemory configurations for client personal computers (110) used with thepresent invention should include at least 1028 megabytes ofsemiconductor random access memory (111) and at least a 200 gigabytehard drive (112). Typical memory configurations for theapplication-server personal computer (120) used with the presentinvention should include at least 5128 megabytes of semiconductor randomaccess memory (121) and at least a 300 gigabyte hard drive (122).Typical memory configurations for the database-server personal computer(130) used with the present invention should include at least 5128megabytes of semiconductor random access memory (131) and at least a 750gigabyte hard drive (132).

Using the system described above, data is extracted from the narrowlyfocused enterprise systems, external databases and the world wide web asrequired to develop a ContextBase (60), develop context frames andmanage performance. Before going further, we need to define a number ofterms that will be used throughout the detailed description of thepreferred embodiment of the Knowledge Based Performance ManagementSystem:

-   -   1. A transaction is any event that is logged or recorded        (actions are a subset of events);    -   2. Transaction data are any data related to a transaction;    -   3. Descriptive data are any data related to an element, factor,        event or commitment. Descriptive data includes forecast data and        other data calculated by the system of the present invention;    -   4. An element of performance (or element) is “an entity or group        that as a result of past transactions, forecasts or other data        has provided and/or is expected to benefit to one or more        organization mission measures”;    -   5. An item is a single member of the group that defines an        element of performance. For example, an individual salesman        would be an “item” in the “element of performance” sales staff.        It is possible to have only one item in an element of        performance;    -   6. Item variables are the transaction data and descriptive data        associated with an item or related group of items;    -   7. Item performance indicators are data derived from transaction        data and/or descriptive data for an item;    -   8. Composite variables for an element are mathematical or        logical combinations of item variables and/or item performance        indicators;    -   9. Element variables or element data are the item variables,        item performance indicators and composite variables for a        specific element or sub-element of performance;    -   10. External factors (or factors) are numerical indicators of:        conditions external to the enterprise, conditions of the        enterprise compared to external expectations of enterprise        conditions or the performance of the enterprise compared to        external expectations of enterprise performance;    -   11. Factor variables are the transaction data and descriptive        data associated with external factors;    -   12. Factor performance indicators are data derived from factor        transaction data and/or descriptive data;    -   13. Composite factors are mathematical or logical combinations        of factor variables and/or factor performance indicators for a        factor;    -   14. Factor data are defined as the factor variables, factor        performance indicators and composite factors;    -   15. A layer is software and/or information that gives an        application, system or layer the ability to interact with        another layer, system, application or set of information at a        general or abstract level rather than at a detailed level;    -   16. An organization is defined as an entity with a mission and        one or more defined, quantified mission measures, organizations        include multi-enterprise organizations and enterprises;    -   17. An organization level is defined as a subset of an        organization characterized by a unique, defined, quantifiable        mission measure, organization levels include divisions,        departments, teams and individuals;    -   18. A value chain is defined by two or more organizations that        have joined together to complete one or more actions;    -   19. A combination is defined by two or more organizations that        have joined together to plan and/or complete one or more actions        (value chains are a subset of combinations);    -   20. Frames are sub-sets of an organization level that can be        analyzed separately. For example, one frame could group together        all the elements and external factors by process allowing each        process in an organization to be analyzed by outside vendors.        Another frame could exclude the one mission measure from each        enterprise within a multi-enterprise organization. Frames can        also be used to store short and long term plan information;    -   21. Context frames include all information relevant to mission        measure performance for a defined combination of context layers,        organization levels and organizations. Context frames can exist        as virtual databases that point to the relevant information in        one or more databases, they can be designated by adding tags to        stored data or they can be physically established as one or more        tables within a database. In the preferred embodiment, each        context frame is a series of pointers (like a virtual database)        that are stored within a separate table;    -   22. Full context frames are context frames that contain all        relevant data from the six context layers (physical, tactical,        instant, organization, mission and social environment) for a        specified organization level;    -   23. Administrative context frames are context frames that        contain all relevant data from the first three context layers        (physical, tactical and instant) for a specified organization        level;    -   24. Strategic context frames are context frames that contain all        relevant data from the last three context layers (organization,        mission and social environment) for a specified organization        level;    -   25. Complete Context is a designation for applications with a        context quotient of 200 that can process full context frames;    -   26. ContextBase is a database that organizes data by context        layer;    -   27. Risk is defined as events or variability that cause reduced        performance;    -   28. Total risk for an organization with publicly traded equity        is defined by the implied volatility associated with        organization equity. The amount of implied volatility can be        determined by analyzing the option prices for organization        equity. For organizations without publicly traded equity, total        risk is the sum of all variability risks and event risks;    -   29. Variability risk is a subset of total risk. It is the risk        of reduced or impaired performance caused by variability in        external factors and/or elements of performance. Variability        risk is generally quantified using statistical measures like        standard deviation per month, per year or over some other time        period. The covariance between different variability risks is        also determined as simulations require quantified information        regarding the inter-relationship between the different risks to        perform effectively;    -   30. Factor variability (or factor variability risk) is a subset        of variability risk. It is the risk of reduced performance        caused by external factor variability;    -   31. Element variability (or element variability risk) is a        subset of variability risk. It is the risk of reduced        performance caused by the variability of an element of        performance;    -   32. Base market risk is a subset of factor variability risk for        an organization with publicly traded equity. It is defined as        the implied variability associated with a portfolio that        represents the market. For example, the S&P 500 can be used in        the U.S. and the FTSE 100 can be used in the U.K. The implied        amount of this variability can be determined by analyzing the        option prices for the portfolio;    -   33. Industry market risk is a subset of factor variability risk        for an organization with publicly traded equity. It is defined        as the implied variability associated with a portfolio that is        in the same SIC code as the organization—industry market risk        can be substituted for base market risk in order to get a        clearer picture of the market risk specific to stock for an        organization;    -   34. Market volatility is a subset of total risk for an        organization with publicly traded equity. It is defined as the        difference between market variability risk and the calculated        values of: base market risk, factor variability, element        variability, event risk (includes strategic event risk and        contingent liabilities) over a given time period;    -   35. Event risk is a subset of total risk. It is the risk of        reduced performance caused by an event. Most insurance policies        cover event risks. For example, an insurance policy might state        that: if this event happens, then we will reimburse event        related expenses up to a pre-determined amount. Other event        risks including customer defection, employee resignation and        supplier bankruptcy are generally overlooked by traditional risk        management systems;    -   36. Standard event risk is a subset of event risk. It is the        risk associated with events that have a one time impact;    -   37. Extreme event risk is a subset of event risk. It is the risk        associated with events that have a one time impact three or more        standard deviations above the average impact for an event;    -   38. Contingent liabilities are a subset of event risk. They are        liabilities the organization may have at some future date where        the liability is contingent on some event occurring in the        future, therefore they can be considered as a type of event        risk. They are different from standard event risks in that the        amount of “damage” is often defined contractually and is known        in advance. Many feel that the bankruptcy of Enron was triggered        by a contingent liability from one of the infamous “off balance        sheet entities”. The system of the present invention quantifies        contingent liabilities for all organization levels—even if the        liability comes from a entity that isn't on the balance sheet—a        distinct advantage over current financial systems;    -   39. Strategic risk (or strategic event risk) is a subset of        event risk. It is the risk associated with events that can have        a permanent impact on the performance of an enterprise or        organization. Examples of strategic risk would include: the risk        that a large new competitor enters the market and the risk that        a new technology renders existing products obsolete;    -   40. Real options are defined as options the organization may        have to make a change in its operation at some future date—these        can include the introduction of a new product, the ability to        shift production to lower cost environments, etc. Real options        are generally supported by the elements of performance of an        organization;    -   41. Narrow systems are the systems listed in Table 3 and any        other system that supports the analysis, measurement or        management of an element, event, commitment or priority of an        organization or enterprise; and    -   42. The efficient frontier is the curve defined by the maximum        performance the organization can expect for given levels of        risk.        We will use the terms defined above when detailing the preferred        embodiment of the present invention. In this invention, analysis        bots are used to determine element of performance lives and the        percentage of mission measure performance that is attributable        to each element of performance organization level. The resulting        values are then added together to determine the contribution of        each element of performance to the mission performance at each        organization level. External factor contributions and risk        impacts are calculated in a similar manner, however, they        generally do not have defined lives.

As discussed previously, the Knowledge Based Performance ManagementSystem completes processing in three distinct stages. As shown in FIG.6A, FIG. 6B, FIG. 6C and FIG. 6D the first stage of processing (block200 from FIG. 1) extracts data, defines mission measures and preparesdata for the next stage of processing. As shown in FIG. 7A, FIG. 7B,FIG. 7C, FIG. 7D and FIG. 7E the second stage of processing (block 300from FIG. 1) develops and then continually updates the mission-orientedContextBase (60) by organization and organization level. As shown inFIG. 8, in the third and final stage of processing (block 400 fromFIG. 1) prepares context frames for use by the standard applications andoptionally prepares and print reports. If the operation is continuous,then the processing described above is continuously repeated.

Mission Measure Specification

The flow diagram in FIG. 6A, FIG. 6B, FIG. 6C and FIG. 6D details theprocessing that is completed by the portion of the application software(200) that establishes a virtual database for data from other systemsthat is available for processing, prepares unstructured data forprocessing and accepts user (20) and management (21) input as requiredto define the mission measures for each organization level. As discussedpreviously, the system of the present invention is capable of acceptingdata from all the narrowly focused systems listed in Table 3. Dataextraction, processing and storage is completed by organization andorganization level. Operation of the system (100) will be illustrated bydescribing the extraction and use of structured data from a narrowsystem database (5) for supply chain management and an external database(20). A brief overview of the information typically obtained from thesetwo databases will be presented before reviewing each step of processingcompleted by this portion (200) of the application software.

Supply chain systems are one of the seventy plus narrow systemsidentified in Table 3. Supply chain databases are a type of narrowsystem database (5) that contain information that may have been inoperation management system databases in the past. These systems provideenhanced visibility into the availability of resources and promoteimproved coordination between organizations and their suppliers. Allsupply chain systems would be expected to track all of the resourcesordered by an organization after the first purchase. They typicallystore information similar to that shown below in Table 7.

TABLE 7 Supply chain system information  1. Stock Keeping Unit (SKU)  2.Vendor  3. Total quantity on order  4. Total quantity in transit  5.Total quantity on back order  6. Total quantity in inventory  7.Quantity available today  8. Quantity available next 7 days  9. Quantityavailable next 30 days 10. Quantity available next 90 days 11. Quotedlead time 12. Actual average lead time

External databases (20) are used for obtaining information that enablesthe definition and evaluation of elements of performance, externalfactors and event risks. In some cases, information from these databasescan be used to supplement information obtained from the other databasesand the Internet (5 and 10). In the system of the present invention, theinformation extracted from external databases (20) includes the datalisted in Table 8.

TABLE 8 External database information 1. Text information such as thatfound in the Lexis Nexis database; 2. Text information from databasescontaining past issues of    specific publications, 3. Geospatial data;4. Multimedia information such as video and audio clips; and 5. Eventrisk data including information about risk probability and    magnitude

System processing of the information from the different databases (5, 10and 20) and the World Wide Web (25) described above starts in a block202, FIG. 6A. The software in block 202 prompts the user (20) via thesystem settings data window (701) to provide system setting information.The system setting information entered by the user (20) is transmittedvia the network (45) back to the application-server (120) where it isstored in the system settings table (140) in the application database(50) in a manner that is well known. The specific inputs the user (20)is asked to provide at this point in processing are shown in Table 9.

TABLE 9*  1. Continuous, If yes, new calculation frequency? (by minute,hour,    day, week, etc.)  2. Organization(s) (can include partners)  3.Organization structure(s) (organization levels, combinations)  4.Organization industry classification(s) (SIC Code)  5. Names of primarycompetitors by SIC Code  6. Base account structure  7. Base units ofmeasure  8. Base currency  9. Knowledge capture from subject matterexpert? (yes or no) 10. Event models? (yes or no) 11. Instant impactmodels? (yes or no) 12. Video data extraction? (yes or no 13. Internetdata extraction? (yes or no) 14. Text data analysis? (if yes, thenspecify maximum number of    relevant words) 15. Geo-coded data? (ifyes, then specify standard) 16. Maximum number of generations to processwithout improving fitness 17. Maximum number of clusters (default issix) 18. Management report types (text, graphic or both) 19. Missingdata procedure (chose from selection) 20. Maximum time to wait for userinput 21. Maximum number of sub elements 22. Most likely scenario,normal, extreme or mix (default is normal) 23. Simulation time periods24. Risk free interest rate 25. Date range for history-forecast timeperiods (optional) 26. Minimum working capital level (optional)*settings over 4 for each organization level (if different)

The system settings data are used by the software in block 202 toestablish organization levels and context layers. As describedpreviously, there are six context layers for each organization level.The application of the remaining system settings will be furtherexplained as part of the detailed explanation of the system operation.The software in block 202 also uses the current system date to determinethe time periods (generally in months) that require data to complete thecalculations. In the preferred embodiment the analysis of organizationlevel performance by the system utilizes data from every data source forthe four year period before and the three year forecast period after thedate of system calculation. The user (20) also has the option ofspecifying the data periods that will be used for completing systemcalculations. After the date range is calculated it is stored in thesystem settings table (140) in the application database (50), processingadvances to a software block 203.

The software in block 203 prompts the user (20) via the organizationlayer data window (702) to define the different organization levels,define process maps, identify the elements and factors relevant to eachorganization level and graphically depict the relationship between thedifferent organization levels that were saved in the system settings(140). For example, an organization could have two enterprises with eachenterprise having three departments as shown in FIG. 14. In the caseshown in FIG. 14 there would be nine organization levels as shown inTable 10.

TABLE 10 Organization Level Location in example hierarchy 1.Organization Highest Level 2. Enterprise A Middle Level 3. EnterpriseA - Department 100 Lowest Level 4. Enterprise A - Department 200 LowestLevel 5. Enterprise A - Department 300 Lowest Level 6. Enterprise BMiddle Level 7. Enterprise B - Department 101 Lowest Level 8. EnterpriseB - Department 201 Lowest Level 9. Enterprise B - Department 301 LowestLevel

In the system of the present invention an item within an element ofperformance is the lowest organization level. The organization level andprocess map relationships identified by the user (20) are stored in theorganization layer table (174) in the ContextBase (60). It is alsopossible to obtain the organization layer information directly fromnarrow system input. The element and factor definitions by organizationlevel are stored in the element definition table (184) and the factordefinition table (185) in the ContextBase (60) After the data is stored,processing advances to a software block 204.

The software in block 204 communicates via a network (45) with thedifferent databases (5, 10, and 20) that are providing data to theKnowledge Based Performance Management System. As described previously,a number of methods can be used to identify the different data sourcesand make the information available for processing including bulk dataextraction and point to point data extraction using bots or ETL(extract, test and load) utilities. Data from the lower levels of thehierarchy are automatically included in the context layers for thehigher organization levels. In the preferred embodiment the systemsproviding data are identified using UDDI protocols. The databases inthese systems (5, 10 and 20) use XML tags that identify the organizationlevel, context layer, element assignment and/or factor association foreach piece of data. In this stage of processing the software in block204 stores the location information for the data of interest as requiredto establish a virtual database for the administrative layers for eachorganization level that was specified in the system settings table(140). Establishing a virtual database eliminates the latency that cancause problems for real time processing. The virtual databaseinformation for the physical layer for each organization level is storedin the physical layer table (171) in the ContextBase (60). The virtualdatabase information for the tactical layer for each organization levelis stored in the tactical layer table (172) in the ContextBase (60). Thevirtual database information for the instant layer for each organizationlevel is stored in the instant impact layer table (173) in theContextBase (60). Structured data that was made available for processingthat could not be mapped to an administrative context layer,organization level, factor and/or element is stored in the structureddata table (176) in the Context Base (60). World Wide Web data thatneeds to be processed before being mapped to a context layer,organization level, factor and/or element are identified using a virtualdatabase stored in the Internet data table (177) in the ContextBase(60). Video data that needs to be processed before being mapped to acontext layer, organization level, factor and/or element are identifiedusing a virtual database stored in the video data table (178) in theContextBase (60). Unstructured text data that needs to be processedbefore being mapped to a context layer, organization level, factor and/or element are identified using a virtual database stored in the textdata table (180) in the ContextBase (60). Geo-coded data that needs tobe processed before being mapped to a context layer, organization level,factor and/ or element are identified using a virtual database stored inthe geo data table (181) in the ContextBase (60). In all cases, datafrom narrow partner system databases (10) can be extracted and stored ina manner similar to that described for organization narrow system data.This data can include feature designations that define the acceptablerange for data that are changed during optimization calculations. Aftervirtual databases have been created that fully account for all availabledata from the databases (5, 10 and 20) and the World Wide Web (25),processing advances to a software block 205 and then on to a softwareblock 210.

The software in block 210 prompts the user (20) via the review datawindow (703) to review the elements and factors by context layer thathave been identified in the first few steps of processing. Theelement—context layer assignments and the factor—context layerassignments were created by mapping data to their “locations” within theContextBase (60) using xml tag designations. The user (20) has theoption of changing these designations on a one time basis orpermanently. Any changes the user (20) makes are stored in the table forthe corresponding context layer (i.e. tactical layer changes are savedin the tactical layer table (172), etc.). As part of the processing inthis block, the user (20) is given the option to establish datacategories for each context layer using an interactive GEL algorithmthat guides the process of category development. The newly definedcategories are mapped to the appropriate data in the appropriate contextlayer and stored in the organization layer table (174) in theContextBase (60). The user (20) is also prompted by the review datawindow (703) to use data and/or the newly created data categories fromeach context layer to define six of the nine key terms—element, agent,asset, resource, action and commitment (mission measures and prioritieswill be defined in the next step) for each organization level. Theresulting definitions are saved in the key terms table (170) in theContextBase (60) by organization and organization level. Finally, theuser (20) is prompted to define transaction data that do not correspondto one of the six key terms. For example, transaction data may relate toa cell phone call or an email—both events that are not defined asactions for the current organization level. The user (20) will definethese events using standardized definitions from a Thesaurus table (142)in the application database (50) with synonyms that match businessconcepts like “transfer”, “return” and “expedite” as required to defineeach transaction. The new definitions are also stored in the key termstable (170) in the ContextBase (60) before processing advances to asoftware block 215.

The software in block 215 prompts the manager (21) via the missionmeasure data window (704) to use the key term definitions established inthe prior processing step to specify one or more mission measures foreach organization level. As discussed previously, the manager (21) isgiven the option of using pre-defined mission measures for evaluatingthe performance of a commercial organization or defining new missionmeasures using internal and/or external data. If more than one missionmeasure is defined for a given organization level, then the manager (21)is prompted to assign a weighting or relative priority to the differentmission measures that have been defined. The software in this block alsoprompts the manager (21) to identify keywords that are relevant tomission performance for each organization level in each organization.After the mission measure definitions are completed, the value of thenewly defined mission measures are calculated using historical data andforecast data and stored in the mission layer table (175) byorganization and organization level. After this has been completed, themission measure definitions, priorities and keywords are stored in thekey terms table (170) in the ContextBase (60) by organization andorganization level before processing advances to a software block 231.

The software in block 231 checks the structured data table (176) in theContextBase (60) to see if there is any structured data that has notbeen assigned to an organization level and/or context layer. If there isno structured data without a complete assignment (organization,organization level, context layer and element or factor assignmentconstitutes a complete assignment), then processing advances to asoftware block 232. Alternatively, if there are structured data withoutan assignment, then processing advances to a software block 235.

The software in block 235 prompts the manager (21) via theidentification and classification data window (705) to identify thecontext layer, organization level, element assignment or factorassignment for the structured data in table 176. After assignments havebeen specified for every data element, the resulting assignment arestored in the appropriate context layer table in the ContextBase (60) byorganization and organization level before processing advances to asoftware block 232.

The software in block 232 checks the system settings table (140) in theApplication Database (50) to see if video data extraction is going to beused in the current analysis. If video data extraction is not beingused, then processing advances to a software block 236. Alternatively,if video data extraction is being used, then processing advances to asoftware block 233.

The software in block 233 extracts text from the video data stored inthe video data table (178) and stores the resulting text in the texttable (180) in the ContextBase (60). The information in the video comesin two parts, the narrative associated with the image and the imageitself. In the preferred embodiment, the narrative portion of the videohas been captured in captions. These captions along with informationidentifying the time of first broadcast are stored in the text table(180). This same procedure can also be used for capturing data fromradio broadcasts. If captions are not available, then any of a number ofcommercially available voice recognition programs can be used to createtext from the narratives. The image portion of the video requiresconversion. The conversion of video into text requires the use ofseveral conversion algorithms and a synthesis of the results from eachof the different algorithms using a data fusion algorithm. Thealgorithms used for video conversion include: coefficient energy blockclassification, local stroke detection and merge and graphics/text blockclassification. Again, the resulting text information along withinformation identifying the time of first broadcast are stored in thetext table (180) before processing advances to a software block 236.

The software in block 236 checks the system settings table (140) in theApplication Database (50) to see if internet data extraction is going tobe used in the current analysis. If internet data extraction is notbeing used, then processing advances to a software block 241.Alternatively, if internet data extraction is being used, thenprocessing advances to a software block 237.

The software in block 237 checks the bot date table (141) anddeactivates internet text and linkage bots with creation dates beforethe current system date and retrieves information from the key termstable (180). The software in block 237 then initializes text bots foreach keyword stored in the key terms table. The bots are programmed toactivate with the frequency specified by user (20) in the systemsettings table (140).

Bots are independent components of the application that have specifictasks to perform. In the case of internet text and linkage bots, theirtasks are to locate and extract keyword matches and linkages from theWorld Wide Web (25) and then store the extracted text in the text datatable (180) and the linkages in the internet linkages table (177) in theContextBase (60). Every Internet text and linkage bot contains theinformation shown in Table 11.

TABLE 11 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Storagelocation 4. Mapping information 5. Home URL 6. Linkage URL (if any) 7.Organization 8. Organization Level 9. KeywordAfter being initialized, the text and linkage bots locate, extract andstore text and linkages from the World Wide Web (25) in accordance withtheir programmed instructions with the frequency specified by user (20)in the system settings table (140). These bots will continually extractdata as system processing advances a software block 241.

The software in block 241 checks the system settings table (140) to seeif text data analysis is being used. If text data analysis is not beingused, then processing advances to a block 246. Alternatively, if thesoftware in block 241 determines that text data analysis is being used,processing advances to a software block 242.

The software in block 242 checks the bot date table (141) anddeactivates text relevance bots with creation dates before the currentsystem date and retrieves information from the system settings table(140), the key terms table (170) and the text data table (180). Thesoftware in block 242 then initializes text relevance bots to activatewith the frequency specified by user (20) in the system settings table(140). Bots are independent components of the application that havespecific tasks to perform. In the case of text relevance bots, theirtasks are to calculate a relevance measure for each word in the textdata table (180) and to identify the type of word (Name, Proper Name,Verb, Adjective, Complement, Determinant or Other). The relevance ofeach word is determined by calculating a relevance measure using theformula shown in Table 12.

TABLE 12 Relevance (word) = √N · (nm′ − n′m)/√(n + n′)(n + m)(n′ +m′)(m + m′) where N = total number of phrases (n + n′ + m + m′) n =number of relevant phrases where word appears n′ = number of irrelevantphrases where word appears m = number of relevant phrases where worddoes not appear m′ = number of irrelevant phrases where word does notappear Note: relevance is determined by the presence of a keyword in aphrase.One advantage of this approach is that it takes into account the factthat text is generally a sequence of words and not just a “bag ofwords”. The type of word is determined by using a probabilistic speechtagging algorithm. If the amount of text that needs processing is verylarge, then a multi layer neural net can be used to sort the text intoblocks that should be processed and those that should not. Every textrelevance bot contains the information shown in Table 13.

TABLE 13 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization Layer 7.WordAfter being activated, the text relevance bots determine the relevanceand type of each word with the frequency specified by the user (20) inthe system settings table (140). The relevance of each word is stored inthe text data table (180) before processing passes to a software block244.

The software in block 244 checks the bot date table (141) anddeactivates text association bots with creation dates before the currentsystem date and retrieves information from the system settings table(140), the tactical layer table (172), the instant impact layer table(173), the mission measure table (175), the text table (180), theelement definition table (184) and the factor definition table (185).The software in block 244 then initializes text association bots for thewords identified in the prior stage of processing in order of relevanceup to the maximum number for each organization (the user (20) specifiedthe maximum number of keywords in the system settings table). Bots areindependent components of the application that have specific tasks toperform. In the case of text association bots, their tasks are todetermine which element or factor the relevant words are most closelyassociated with. Every bot initialized by software block 244 will storethe association it discovers with the most relevant words stored in thetext data table (180). Every text association bot contains theinformation shown in Table 14.

TABLE 14 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Storagelocation 4. Organization 5. Organization Level 6. WordAfter being initialized, the bots identify the element or factor thateach word is most closely associated with and stores the association“assignment” in the text data table (180) and the element definitiontable (184) or factor definition table (185) in the ContextBase (60)before processing advances to a software block 245.

The software in block 245 prompts the user (20) via the review datawindow (703) to review the associations developed in the prior step inprocessing. Options the user (20) can choose for modifying theassociations include: changing the association to another element orfactor, removing the assigned association, or adding an association toone or more other elements or factors. When all the user (20) completesthe review of the assignments, all changes are stored in the text datatable (180), the element definition table (184) and/or the factordefinition table (185) before system processing advances to a softwareblock 246.

The software in block 246 checks the system settings table (140) in theApplication Database (50) to see if geo-coded data is going to be usedin the current analysis. If geo-coded data is not being used, thenprocessing advances to a software block 251. Alternatively, if geo-codeddata is being used, then processing advances to a software block 247.

The software in block 247 retrieves the data stored in the geo table(181), converts the data in accordance with applicable geo-codingstandard, calculates pre-defined attributes and stores the resultingdata in the physical context layer table (171) by element or factor inthe ContextBase (60) before processing advances to software block 251.

The software in block 251 checks each of the administrative contextlayer tables—the physical layer table (171), the tactical layer table(172) and the instant impact layer table (173)—and the socialenvironment layer table (179) in the ContextBase (60) to see if data ismissing for any required time period. If data is not missing for anyrequired time period, then processing advances to a software block 256.Alternatively, if data for one or more of the required time periods ismissing for one or more of the administrative context layers, thenprocessing advances to a software block 255.

The software in block 255 prompts the user (20) via the review datawindow (703) to specify the method to be used for filling the blanks foreach field that is missing data. Options the user (20) can choose forfilling the blanks include: the average value for the item over theentire time period, the average value for the item over a specifiedperiod, zero, the average of the preceding item and the following itemvalues and direct user input for each missing item. If the user (20)does not provide input within a specified interval, then the defaultmissing data procedure specified in the system settings table (140) isused. When all the blanks have been filled and stored for all of themissing data, system processing advances to a block 256.

The software in block 256 calculates pre-defined attributes by item foreach numeric, item variable in each of the administrative context layertables—the physical layer table (171), the tactical layer table (172) orthe instant impact layer table (173)—in the ContextBase (60) by element.The attributes calculated in this step include: summary data likecumulative total value; ratios like the period to period rate of changein value; trends like the rolling average value, comparisons to abaseline value like change from a prior years level and time laggedvalues like the time lagged value of each numeric item variable. Thesoftware in block 256 also derives attributes for each item datevariable in each of the administrative context layer tables (171, 172and 173) in the ContextBase (60). The derived date variables includesummary data like time since last occurrence and cumulative time sincefirst occurrence; and trends like average frequency of occurrence andthe rolling average frequency of occurrence. The software in block 256derives similar attributes for the text and geospatial item variablesstored in the administrative context layer tables—the physical layertable (171), the tactical layer table (172) or the instant impact layertable (173)—by element. The numbers derived from the item variables arecollectively referred to as “item performance indicators”. The softwarein block 256 also calculates pre-specified combinations of variablescalled composite variables for measuring the strength of the differentelements of performance. The item performance indicators and thecomposite variables are tagged and stored in the appropriateadministrative context layer table—the physical layer table (171), thetactical layer table (172) or the instant impact layer table (173)—byelement and organization level before processing advances to a softwareblock 257.

The software in block 257 uses attribute derivation algorithms such asthe AQ program to create combinations of variables from theadministrative context layer tables—the physical layer table (171), thetactical layer table (172) or the instant impact layer table (173)—thatwere not pre-specified for combination in the prior processing step.While the AQ program is used in the preferred embodiment of the presentinvention, other attribute derivation algorithms, such as the LINUSalgorithms, may be used to the same effect. The resulting compositevariables are tagged and stored in the appropriate administrativecontext layer table—the physical layer table (171), the tactical layertable (172) or the instant impact layer table (173)—in the ContextBase(60) by element before processing advances to a software block 260.

The software in block 260 derives external factor indicators for eachfactor numeric data field stored in the social environment layer table(179). For example, external factors can include: the ratio ofenterprise earnings to expected earnings, the number and amount of juryawards, commodity prices, the inflation rate, growth in gross domesticproduct, enterprise earnings volatility vs. industry average volatility,short and long term interest rates, increases in interest rates, insidertrading direction and levels, industry concentration, consumerconfidence and the unemployment rate that have an impact on the marketprice of the equity for an enterprise and/or an industry. The externalfactor indicators derived in this step include: summary data likecumulative totals, ratios like the period to period rate of change,trends like the rolling average value, comparisons to a baseline valuelike change from a prior years price and time lagged data like timelagged earnings forecasts. In a similar fashion the software in block260 calculates external factors for each factor date field in the socialenvironment layer table (179) including summary factors like time sincelast occurrence and cumulative time since first occurrence; and trendslike average frequency of occurrence and the rolling average frequencyof occurrence. The numbers derived from numeric and date fields arecollectively referred to as “factor performance indicators”. Thesoftware in block 260 also calculates pre-specified combinations ofvariables called composite factors for measuring the strength of thedifferent external factors. The factor performance indicators and thecomposite factors are tagged and stored in the social environment layertable (179) by factor and organization level before processing advancesto a block 261.

The software in block 261 uses attribute derivation algorithms, such asthe Linus algorithm, to create combinations of the external factors thatwere not pre-specified for combination in the prior processing step.While the Linus algorithm is used in the preferred embodiment of thepresent invention, other attribute derivation algorithms, such as the AQprogram, may be used to the same effect. The resulting compositevariables are tagged and stored in the in the social environment layertable (179) by factor and organization level before processing advancesto a block 262.

The software in block 262 checks the bot date table (141) anddeactivates pattern bots with creation dates before the current systemdate and retrieves information from the system settings table (140), thephysical layer table (171), the tactical layer table (172), the instantimpact layer table (173) and the social environment layer table (179).

The software in block 262 then initializes pattern bots for each layerto identify frequent patterns in each layers. Bots are independentcomponents of the application that have specific tasks to perform. Inthe case of pattern bots, their tasks are to identify and frequentpatterns in the data for each context layer, element, factor andorganization level. In the preferred embodiment, pattern bots use theApriori algorithm to identify patterns including frequent patterns,sequential patterns and multi-dimensional patterns. However, a number ofother pattern identification algorithms including the PASCAL algorithmcan be used alone or in combination to the same effect. Every patternbot contains the information shown in Table 15.

TABLE 15 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Storagelocation 4. Organization 6. Context Layer, Element, Factor orOrganization level 7. AlgorithmAfter being initialized, the bots identify patterns in the data byelement, factor, layer or organization level. Each pattern is given aunique identifier and the frequency and type of each pattern isdetermined. The numeric values associated with the patterns are itemperformance indicators. The values are stored in the appropriate contextlayer table by element or factor. When data storage is complete,processing advances to a software block 303.

ContextBase Development

The flow diagrams in FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D and FIG. 7Edetail the processing that is completed by the portion of theapplication software (300) that continually develops a mission orientedContextBase (60) by creating and activating analysis bots that:

-   -   1. Supplement the organization layer information provided        previously by identifying inter-relationships between the        different elements of performance, external factors and risks;    -   2. Complete the mission measure layer of the ContextBase (60) by        developing robust models of the elements, factors and risks        driving mission measure performance;    -   3. Optionally, develop robust models of the elements, factors        and risks driving action occurrence rates;    -   4. Optionally, develop robust models of the elements, factors        and risks causing instant impact levels to vary, and    -   5. Combine the mission measure analyses by organization and        organization level as required to evaluate strategic alignment        and determine the relationship between the mission measures and        mission performance.

Each analysis bot generally normalizes the data being analyzed beforeprocessing begins. As discussed previously, processing in the preferredembodiment includes an analysis of all mission measures by organizationand organization level. It is to be understood that the system of thepresent invention can combine any number of mission measures as requiredto evaluate the performance of any organization level.

Processing in this portion of the application begins in software block301. The software in block 301 checks the mission layer table (175) inthe ContextBase (60) to determine if there are current models for allmission measures for every organization level. If all the missionmeasure models are current, then processing advances to a software block321. Alternatively, if all mission measure models are not current, thenthe next mission measure for the next organization level is selected andprocessing advances to a software block 303. The software in block 303retrieves the previously calculated values for the mission measure fromthe mission layer table (175) before processing advances to a softwareblock 304.

The software in block 304 checks the bot date table (141) anddeactivates temporal clustering bots with creation dates before thecurrent system date. The software in block 304 then initializes bots inaccordance with the frequency specified by the user (20) in the systemsettings table (140). The bot retrieves information from the missionlayer table (175) for the organization level being analyzed and definesregimes for the mission measure being analyzed before saving theresulting cluster information in the mission layer table (175) in theContextBase (60). Bots are independent components of the applicationthat have specific tasks to perform. In the case of temporal clusteringbots, their primary task is to segment mission measure performance intodistinct time regimes that share similar characteristics. The temporalclustering bot assigns a unique identification (id) number to each“regime” it identifies before tagging and storing the unique id numbersin the mission layer table (175). Every time period with data areassigned to one of the regimes. The cluster id for each regime is savedin the data record for the mission measure and organization level beinganalyzed. The time regimes are developed using a competitive regressionalgorithm that identifies an overall, global model before splitting thedata and creating new models for the data in each partition. If theerror from the two models is greater than the error from the globalmodel, then there is only one regime in the data. Alternatively, if thetwo models produce lower error than the global model, then a third modelis created. If the error from three models is lower than from two modelsthen a fourth model is added. The process continues until adding a newmodel does not improve accuracy. Other temporal clustering algorithmsmay be used to the same effect. Every temporal clustering bot containsthe information shown in Table 16.

TABLE 16 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Maximum number of clusters 6.Organization 7. Organization Level 8. Mission Measure

When bots in block 304 have identified and stored regime assignments forall time periods with mission measure data for the current organization,processing advances to a software block 305.

The software in block 305 checks the bot date table (141) anddeactivates variable clustering bots with creation dates before thecurrent system date. The software in block 305 then initializes bots asrequired for each element of performance and external factor for thecurrent organization level. The bots activate in accordance with thefrequency specified by the user (20) in the system settings table (140),retrieve the information from the physical layer table (171), thetactical layer table (172), the instant impact layer table (173), thesocial environment layer table (179), the element definition table (184)and/or the factor definition table (185) as required and define segmentsfor the element data and factor data before tagging and saving theresulting cluster information in the element definition table (184) orthe factor definition table (185).

Bots are independent components of the application that have specifictasks to perform. In the case of variable clustering bots, their primarytask is to segment the element data and factor data into distinctclusters that share similar characteristics. The clustering bot assignsa unique id number to each “cluster” it identifies, tags and stores theunique id numbers in the element definition table (184) and factordefinition table (185). Every item variable for every element ofperformance is assigned to one of the unique clusters. The cluster idfor each variable is saved in the data record for each variable in thetable where it resides. In a similar fashion, every factor variable forevery external factor is assigned to a unique cluster. The cluster idfor each variable is tagged and saved in the data record for the factorvariable. The element data and factor data are segmented into a numberof clusters less than or equal to the maximum specified by the user (20)in the system settings table (140). The data are segmented using the“default” clustering algorithm the user (20) specified in the systemsettings table (140). The system of the present invention provides theuser (20) with the choice of several clustering algorithms including: anunsupervised “Kohonen” neural network, decision tree, support vectormethod, K-nearest neighbor, expectation maximization (EM) and thesegmental K-means algorithm. For algorithms that normally require thenumber of clusters to be specified, the bot will use the maximum numberof clusters specified by the user (20). Every variable clustering botcontains the information shown in Table 17.

TABLE 17  1. Unique ID number (based on date, hour, minute, second ofcreation)  2. Creation date (date, hour, minute, second)  3. Mappinginformation  4. Storage location  5. Element of performance or externalfactor  6. Clustering algorithm type  7. Organization  8. OrganizationLevel  9. Maximum number of clusters 10. Variable 1 . . . to 10 + n.Variable nWhen bots in block 305 have identified, tagged and stored clusterassignments for the data associated with each element of performance orexternal factor in the element definition table (184) or factordefinition table (185), processing advances to a software block 306.

The software in block 306 checks the mission layer table (175) in theContextBase (60) to see if the current mission measure is an optionsbased measure like contingent liabilities, real options or strategicrisk. If the current mission measure is not an options based measure,then processing advances to a software block 309. Alternatively, if thecurrent mission measure is an options based measure, then processingadvances to a software block 307.

The software in block 307 checks the bot date table (141) anddeactivates options simulation bots with creation dates before thecurrent system date. The software in block 307 then retrieves theinformation from the system settings table (140), the element definitiontable (184) and factor definition table (185) and the scenarios table(152) as required to initialize option simulation bots in accordancewith the frequency specified by the user (20) in the system settingstable (140).

Bots are independent components of the application that have specifictasks to perform. In the case of option simulation bots, their primarytask is to determine the impact of each element and factor on themission measure under different scenarios. The option simulation botsrun a normal scenario, an extreme scenario and a combined scenario. Inthe preferred embodiment, Monte Carlo models are used to complete theprobabilistic simulation, however other probabilistic simulation modelssuch as Quasi Monte Carlo can be used to the same effect. The elementand factor impacts on option mission measures could be determined usingthe processed detailed below for the other types of mission measures,however, in the preferred embodiment a separate procedure is used. Themodels are initialized specifications used in the baseline calculations.Every option simulation bot activated in this block contains theinformation shown in Table 18.

TABLE 18 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Type: normal, extreme or combined 6.Option type: real option, contingent liability or strategic risk 7.Organization 7. Organization level 8. Mission measureAfter the option simulation bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, the bots retrieve the requiredinformation and simulate the mission measure over the time periodsspecified by the user (20) in the system settings table (140) asrequired to determine the impact of each element and factor on themission measure. After the option simulation bots complete theircalculations, the resulting sensitivities are saved in the elementdefinition table (184) and factor definition table (185) by organizationand organization level in the application database (50) and processingadvances to a software block 309.

The software in block 309 checks the bot date table (141) anddeactivates all predictive model bots with creation dates before thecurrent system date. The software in block 309 then retrieves theinformation from the system settings table (140), the mission layertable (175), the element definition table (184) and the factordefinition table (185) as required to initialize predictive model botsfor each mission layer.

Bots are independent components of the application that have specifictasks to perform. In the case of predictive model bots, their primarytask is to determine the relationship between the element and factordata and the mission measure being evaluated. Predictive model bots areinitialized for every organization level where the mission measure beingevaluated is used. They are also initialized for each cluster and regimeof data in accordance with the cluster and regime assignments specifiedby the bots in blocks 304 and 305 by organization and organizationlevel. A series of predictive model bots is initialized at this stagebecause it is impossible to know in advance which predictive model typewill produce the “best” predictive model for the data from eachcommercial enterprise. The series for each model includes 12 predictivemodel bot types: neural network; CART; GARCH, projection pursuitregression; generalized additive model (GAM), redundant regressionnetwork; rough-set analysis, boosted Naive Bayes Regression; MARS;linear regression; support vector method and stepwise regression.Additional predictive model types can be used to the same effect. Everypredictive model bot contains the information shown in Table 19.

TABLE 19 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization Level 7.Global or Cluster (ID) and/or Regime (ID) 8. Element, sub-element orexternal factor 9. Predictive model typeAfter predictive model bots are initialized, the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, the bots retrieve the requireddata from the appropriate table in the ContextBase (60) and randomlypartition the element or factor data into a training set and a test set.The software in block 309 uses “bootstrapping” where the differenttraining data sets are created by re-sampling with replacement from theoriginal training set so data records may occur more than once. Afterthe predictive model bots complete their training and testing, the bestfit predictive model assessments of element and factor impacts onmission measure performance are saved in the element definition table(184) and the factor definition table (185) before processing advancesto a block 310.

The software in block 310 determines if clustering improved the accuracyof the predictive models generated by the bots in software block 309 byorganization and organization level. The software in block 310 uses avariable selection algorithm such as stepwise regression (other types ofvariable selection algorithms can be used) to combine the results fromthe predictive model bot analyses for each type of analysis—with andwithout clustering—to determine the best set of variables for each typeof analysis. The type of analysis having the smallest amount of error asmeasured by applying the mean squared error algorithm to the test dataare given preference in determining the best set of variables for use inlater analysis. There are four possible outcomes from this analysis asshown in Table 20.

TABLE 20 1. Best model has no clustering 2. Best model has temporalclustering, no variable clustering 3. Best model has variableclustering, no temporal clustering 4. Best model has temporal clusteringand variable clusteringIf the software in block 310 determines that clustering improves theaccuracy of the predictive models for an enterprise, then processingadvances to a software block 314. Alternatively, if clustering does notimprove the overall accuracy of the predictive models for an enterprise,then processing advances to a software block 312.

The software in block 312 uses a variable selection algorithm such asstepwise regression (other types of variable selection algorithms can beused) to combine the results from the predictive model bot analyses foreach model to determine the best set of variables for each model. Themodels having the smallest amount of error, as measured by applying themean squared error algorithm to the test data, are given preference indetermining the best set of variables. As a result of this processing,the best set of variables contain the: item variables, item performanceindicators, factor performance indications, composite variables andcomposite factors (aka element data and factor data) that correlate moststrongly with changes in the mission measure being analyzed. The bestset of variables will hereinafter be referred to as the “performancedrivers”.

Eliminating low correlation factors from the initial configuration ofthe vector creation algorithms increases the efficiency of the nextstage of system processing. Other error algorithms alone or incombination may be substituted for the mean squared error algorithm.After the best set of variables have been selected, tagged and stored inthe element definition table (184) and the factor definition table (185)for each organization level, the software in block 312 tests theindependence of the performance drivers for each organization levelbefore processing advances to a block 313.

The software in block 313 checks the bot date table (141) anddeactivates causal predictive model bots with creation dates before thecurrent system date. The software in block 313 then retrieves theinformation from the system settings table (140) and the elementdefinition table (184) and factor definition table (185) as required toinitialize causal predictive model bots for each element of performance,sub-element of performance and external factor in accordance with thefrequency specified by the user (20) in the system settings table (140).

Bots are independent components of the application that have specifictasks to perform. In the case of causal predictive model bots, theirprimary task is to refine the performance driver selection to reflectonly causal variables. The Bayesian bots in this step also refine theestimates of element or factor impact developed by the predictive modelbots in a prior processing step by assigning a probability to the impactestimate. A series of causal predictive model bots are initialized atthis stage because it is impossible to know in advance which causalpredictive model will produce the “best” vector for the best fitvariables from each model. The series for each model includes fivecausal predictive model bot types: Tetrad, MML, LaGrange, Bayesian andpath analysis. The software in block 313 generates this series of causalpredictive model bots for each set of performance drivers stored in theelement definition table (184) and factor definition table (185) in theprevious stage in processing. Every causal predictive model botactivated in this block contains the information shown in Table 21.

TABLE 21  1. Unique ID number (based on date, hour, minute, second ofcreation)  2. Creation date (date, hour, minute, second)  3. Mappinginformation  4. Storage location  5. Component or subcomponent of value 6. Element, sub-element or external factor  7. Variable set  8. Causalpredictive model type  9. Organization 10. Organization levelAfter the causal predictive model bots are initialized by the softwarein block 313, the bots activate in accordance with the frequencyspecified by the user (20) in the system settings table (140). Onceactivated, they retrieve the required information for each model andsub-divide the variables into two sets, one for training and one fortesting. After the causal predictive model bots complete theirprocessing for each model, the software in block 313 uses a modelselection algorithm to identify the model that best fits the data foreach element of performance, sub-element of performance and externalfactor being analyzed. For the system of the present invention, a crossvalidation algorithm is used for model selection. The software in block313 tags and saves the refined estimates of probable impact and the bestfit causal factors in the element definition table (184) or the factordefinition table (185) in the ContextBase (60) before processingadvances to a block 321.

If software in block 310 determines that clustering improves predictivemodel accuracy, then processing advances directly to block 314 asdescribed previously. The software in block 314 uses a variableselection algorithm such as stepwise regression (other types of variableselection algorithms can be used) to combine the results from thepredictive model bot analyses for each model, cluster and/or regime todetermine the best set of variables for each model. The models havingthe smallest amount of error as measured by applying the mean squarederror algorithm to the test data are given preference in determining thebest set of variables. As a result of this processing, the best set ofvariables contains: the element data and factor data that correlate moststrongly with changes in the components of value. The best set ofvariables will hereinafter be referred to as the “performance drivers”.Eliminating low correlation factors from the initial configuration ofthe vector creation algorithms increases the efficiency of the nextstage of system processing. Other error algorithms alone or incombination may be substituted for the mean squared error algorithm.After the best set of variables have been selected, tagged asperformance drivers and stored in the element definition table (184) andfactor definition table (185) for all organization levels, the softwarein block 314 tests the independence of the performance drivers at everyorganization level before processing advances to a block 315.

The software in block 315 checks the bot date table (141) anddeactivates causal predictive model bots with creation dates before thecurrent system datr. The software in block 315 then retrieves theinformation from the system settings table (140) and the elementdefinition table (184) and factor definition table (185) as required toinitialize causal predictive model bots for each element of performance,sub-element of performance and external factor for every organizationlevel in accordance with the frequency specified by the user (20) in thesystem settings table (140).

Bots are independent components of the application that have specifictasks to perform. In the case of causal predictive model bots, theirprimary task is to refine the element and factor performance driverselection to reflect only causal variables. (Note: these variables aregrouped together to represent a single element vector when they aredependent). In some cases it may be possible to skip the correlationstep before selecting causal the item variables, factor variables, itemperformance indicators, factor performance indicators, compositevariables and composite factors (aka element data and factor data). Aseries of causal predictive model bots are initialized at this stagebecause it is impossible to know in advance which causal predictivemodel will produce the “best” vector for the best fit variables fromeach model. The series for each model includes four causal predictivemodel bot types: Tetrad, LaGrange, Bayesian and path analysis. TheBayesian bots in this step also refine the estimates of element orfactor impact developed by the predictive model bots in a priorprocessing step by assigning a probability to the impact estimate. Thesoftware in block 315 generates this series of causal predictive modelbots for each set of performance drivers stored in the elementdefinition table (184) and factor definition table (185) in the previousstage in processing. Every causal predictive model bot activated in thisblock contains the information shown in Table 22.

TABLE 22  1. Unique ID number (based on date, hour, minute, second ofcreation)  2. Creation date (date, hour, minute, second)  3. Mappinginformation  4. Storage location  5. Component or subcomponent of value 6. Cluster (ID) and/or Regime (ID)  7. Element, sub-element or externalfactor  8. Variable set  9. Organization 10. Enterprise 11. Causalpredictive model typeAfter the causal predictive model bots are initialized by the softwarein block 315, the bots activate in accordance with the frequencyspecified by the user (20) in the system settings table (140). Onceactivated, they retrieve the required information for each model andsub-divide the variables into two sets, one for training and one fortesting. The same set of training data are used by each of the differenttypes of bots for each model. After the causal predictive model botscomplete their processing for each model, the software in block 315 usesa model selection algorithm to identify the model that best fits thedata for each element, sub-element or external factor being analyzed bymodel and/or regime by organization and organization level. For thesystem of the present invention, a cross validation algorithm is usedfor model selection. The software in block 315 saves the refined impactestimates and the best fit causal factors in the element definitiontable (184) or the factor definition table (185) in the ContextBase (60)before processing advances to a block 321.

The software in block 321 tests the performance drivers to see if thereis interaction between elements, between elements and external factorsor between external factors by organization and organization level. Thesoftware in this block identifies interaction by evaluating a chosenmodel based on stochastic-driven pairs of value-driver subsets. If theaccuracy of such a model is higher that the accuracy of statisticallycombined models trained on attribute subsets, then the attributes fromsubsets are considered to be interacting and then they form aninteracting set. The software in block 321 also tests the performancedrivers to see if there are “missing” performance drivers that areinfluencing the results. If the software in block 321 does not detectany performance driver interaction or missing variables for eachenterprise, then system processing advances to a block 324.Alternatively, if missing data or performance driver interactions acrosselements are detected by the software in block 321 for one or moremission measure processing advances to a software block 322.

The software in block 322 prompts the user (20) via the structurerevision window (706) to adjust the specification(s) for the elements ofperformance, sub-elements of performance or external factors as requiredto minimize or eliminate the interaction that was identified. At thispoint the user (20) has the option of specifying that one or moreelements of performance, sub elements of performance and/or externalfactors be combined for analysis purposes (element combinations and/orfactor combinations) for each enterprise where there is interactionbetween elements and/or factors. The user (20) also has the option ofspecifying that the elements or external factors that are interactingwill be evaluated by summing the impact of their individual performancedrivers. Finally, the user (20) can choose to re-assign a performancedriver to a new element of performance or external factor to eliminatethe inter-dependency. This process is the preferred solution when theinter-dependent performance driver is included in the going concernelement of performance. Elements and external factors that will beevaluated by summing their performance drivers will not have vectorsgenerated.

Elements of performance and external factors generally do not shareperformance drivers and they are not combined with one another. However,when an external factor and an element of performance are shown to beinter-dependent, it is usually because the element of performance is adependent on the external factor. For example, the performance of aprocess typically varies with the price of commodities consumed in theprocess. In that case, the external factor impact and the element ofperformance would be expected to be a function of the same performancedriver. The software in block 322 examines all the factor-elementdependencies and suggest the appropriate percentage of factor riskassignment to the different elements it interacts with. For example, 30%of a commodity factor risk could be distributed to each of the 3processes that consume the commodity with the remaining 10% staying inthe going concern element of performance. The user (20) either acceptsthe suggested distribution or specifies his own distribution for eachfactor-element interaction. After the input from the user (20) is savedin the system settings table (140), the element definition table (184)and factor definition table (185) system processing advances to asoftware block 323. The software in block 323 checks the system settingstable (140) and the element definition table (184) and factor definitiontable (185) to see if there any changes in structure. If there have beenchanges in the structure, then processing returns to block 201 and thesystem processing described previously is repeated. Alternatively, ifthere are no changes in structure, then the information regarding theelement interaction is saved in the organization layer table (174)before processing advances to a block 324.

The software in block 324 checks the bot date table (141) anddeactivates vector generation bots with creation dates before thecurrent system date. The software in block 324 then initializes bots foreach element of performance, sub-element of performance, elementcombination, factor combination and external factor for each enterprisein the organization. The bots activate in accordance with the frequencyspecified by the user (20) in the system settings table (140), retrievethe information from the system settings table (140), the elementdefinition table (184) and factor definition table (185) as required toinitialize vector generation bots for each element of performance andsub-element of performance in accordance with the frequency specified bythe user (20) in the system settings table (140). Bots are independentcomponents of the application that have specific tasks to perform. Inthe case of vector generation bots, their primary task is to produceformulas, (hereinafter, vectors) that summarize the relationship betweenthe causal performance drivers and changes in the component orsub-component of value being examined for each enterprise. The causalperformance drivers may be grouped by element of performance,sub-element of performance, external factor, factor combination orelement combination. As discussed previously, the vector generation stepis skipped for performance drivers where the user has specified thatperformance driver impacts will be mathematically summed to determinethe value of the element or factor. The vector generation bots useinduction algorithms to generate the vectors. Other vector generationalgorithms can be used to the same effect. The software in block 324generates a vector generation bot for each set of causal performancedrivers stored in the element definition table (184) and factordefinition table (185). Every vector generation bot contains theinformation shown in Table 23.

TABLE 23 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.Element, sub-element, factor or combination 8. Factor 1 . . . to 8 + n.Factor nWhen bots in block 324 have identified, tagged and stored vectors forall time periods with data for all the elements, sub-elements,combinations or external factors where vectors are being calculated inthe element definition table (184) and factor definition table (185) byorganization and organization level, processing advances to a softwareblock 325.

The software in block 325 checks the bot date table (141) anddeactivates element life bots with creation dates before the currentsystem date. The software in block 325 then retrieves the informationfrom the system settings table (140) and the element definition table(184) as required to initialize element life bots for each element andsub-element of performance for each organization level being analyzed.

Bots are independent components of the application that have specifictasks to perform. In the case of element life bots, their primary taskis to determine the expected life of each element and sub-element ofperformance. There are three methods for evaluating the expected life ofthe elements and sub-elements of performance:

-   -   1. Elements of performance that are defined by a population of        members or items (such as: channel partners, customers,        employees and vendors) will have their lives estimated by        analyzing and forecasting the lives of the members of the        population. The forecasting of member lives will be determined        by the “best” fit solution from competing life estimation        methods including the Iowa type survivor curves, Weibull        distribution survivor curves, Gompertz-Makeham survivor curves,        polynomial equations using the methodology for selecting from        competing forecasts disclosed in cross referenced U.S. Pat. No.        5,615,109;    -   2. Elements of performance (such as patents, long term supply        agreements and insurance contracts) that have legally defined        lives will have their lives calculated using the time period        between the current date and the expiration date of the element        or sub-element; and    -   3. Finally, for commercial business evaluations elements of        performance and sub-elements of performance (such as brand        names, information technology and processes) that do not have        defined lives and/or that may not consist of a collection of        members will have their lives estimated as a function of the        enterprise Competitive Advantage Period (CAP).        In the latter case, the estimate will be completed using the        element vector trends and the stability of relative element        strength. More specifically, lives for these element types are        estimated by: subtracting time from the CAP for element        volatility that exceeds enterprise volatility and/or subtracting        time for relative element strength that is below the leading        position and/or relative element strength that is declining. In        all cases, the resulting values are tagged and stored in the        element definition table (184) for each element and sub-element        of performance by organization and organization level. Every        element life bot contains the information shown in Table 24.

TABLE 24 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization Level 7.Element or sub-element of performance 8. Life estimation method (itemanalysis, date calculation or relative to CAP)After the element life bots are initialized, they are activated inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated, the bots retrieveinformation for each element and sub-element of performance from theelement definition table (184) as required to complete the estimate ofelement life. The resulting values are then tagged and stored in theelement definition table (184) by organization and organization level inthe ContextBase (60) before processing advances to a block 326.

The software in block 326 checks the bot date table (141) anddeactivates event risk bots with creation dates before the currentsystem date. The software in the block then retrieves the informationfrom the system settings table (140) and the event risk table (186) asrequired to initialize event risk bots for each organization level inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Bots are independent components of the applicationthat have specific tasks to perform. In the case of event risk bots,their primary tasks are to forecast the frequency of standard eventrisks by organization and organization level and forecast the impact onthe mission measure. In addition to forecasting risks that aretraditionally covered by insurance, the system of the present inventionalso uses the data to forecast standard, “non-insured” event risks suchas the risk of employee resignation and the risk of customer defection.The system of the present invention uses the forecasting methodsdisclosed in cross-referenced U.S. Pat. No. 5,615,109 for standard eventrisk forecasting. Other forecasting methods can be used to the sameeffect. Every event risk bot contains the information shown in Table 25.

TABLE 25 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.Event riskAfter the event risk bots are initialized, the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated the bots, retrieve the datafrom the element definition table (184) and factor definition table(185) and then forecast the frequency and severity of the event risks.The resulting forecasts for each enterprise are then stored in the eventrisk table (186) before processing advances to a software block 327.

The software in block 327 checks the bot date table (141) anddeactivates extreme value bots with creation dates before the currentsystem date. The software in block 327 then retrieves the informationfrom the system settings table (140), the element definition table(184), the factor definition table (185) and the event risk table (186)as required to initialize extreme value bots in accordance with thefrequency specified by the user (20) in the system settings table (140).Bots are independent components of the application that have specifictasks to perform. In the case of extreme value bots, their primary taskis to forecast the probability of realizing extreme values and identifythe range of extreme values for every event risk, action and causal,performance driver (for both elements of performance and externalfactors). The extreme value bots use the Blocks method and the peak overthreshold method to identify extreme values. Other extreme valuealgorithms can be used to the same effect. Every extreme value botactivated in this block contains the information shown in Table 26.

TABLE 26 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.Method: blocks or peak over threshold 8. Event risk, performance driveror actionAfter the extreme value bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and forecast the likelihood of realizing extreme values anddetermine the extreme value range for each performance driver or eventrisk. The bot tags and saves the extreme values for each causalperformance driver in the element definition table (184) or the factordefinition table (185) by organization and organization level. Theextreme event risk information is stored in the event risk table (186)by organization and organization level in the ContextBase (60) beforeprocessing advances to a software block 328.

The software in block 328 checks the bot date table (141) anddeactivates strategic event bots with creation dates before the currentsystem date. The software in block 328 then retrieves the informationfrom the system settings table (140), the element definition table(184), the factor definition table (185) and the event risk table (186)as required to initialize strategic event bots in accordance with thefrequency specified by the user (20) in the system settings table (140).Bots are independent components of the application that have specifictasks to perform. In the case of strategic event bots, their primarytask is to identify the probability and magnitude of strategic eventsthat can impact mission measure performance for each organization level.The strategic event bots use game theoretic real option models toforecast strategic risks.. Other risk forecasting algorithms can be usedto the same effect. Every strategic event bot activated in this blockcontains the information shown in Table 27.

TABLE 27 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization levelAfter the strategic event bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and forecast the frequency and magnitude of strategicevents. The bots save the strategic event forecast information in theevent risk table (186) by organization and organization level in theContextBase (60) and processing advances to a block 329.

The software in block 329 checks the bot date table (141) anddeactivates statistical bots with creation dates before the currentsystem date. The software in block 329 then retrieves the informationfrom the system settings table (140), the element definition table(184), the factor definition table (185) and the event risk table (186)as required to initialize statistical bots for each causal performancedriver and event risk. Bots are independent components of theapplication that have specific tasks to perform. In the case ofstatistical bots, their primary tasks are to calculate and storestatistics such as mean, median, standard deviation, slope, averageperiod change, maximum period change, variance and covariance betweeneach causal performance driver and event risk. Every statistical botcontains the information shown in Table 28.

TABLE 28 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.Performance driver or event riskThe bots in block 329 calculate and store statistics for each causalperformance driver and event risk in the element definition table (184),factor definition table (185) or event risk table (186) by organizationand organization level. The covariance information is also stored in theorganization layer table (174) before processing advances to a softwareblock 331.

The software in block 331 checks the bot date table (141) anddeactivates forecast update bots with creation dates before the currentsystem date. The software in block 331 then retrieves the informationfrom the system settings table (140) and factor definition table (185)as required to initialize forecast bots in accordance with the frequencyspecified by the user (20) in the system settings table (140). Bots areindependent components of the application that have specific tasks toperform. In the case of forecast update bots, their task is to comparethe forecasts for external factors and with the information availablefrom futures exchanges and update the existing forecasts as required.Every forecast update bot activated in this block contains theinformation shown in Table 29.

TABLE 29 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.External factor 8. Forecast time periodAfter the forecast update bots are initialized, they activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Once activated, they retrieve the requiredinformation and determine if any forecasts need to be updated to bringthem in line with the market data on future values. The bots save theupdated factor forecasts in the factor definition table (185) byorganization and organization level and processing advances to asoftware block 334.

The software in block 334 checks the bot date table (141) anddeactivates scenario bots with creation dates before the current systemdate. The software in block 334 then retrieves the information from thesystem settings table (140), the element definition table (184), thefactor definition table (185) and the event risk table as required toinitialize scenario bots in accordance with the frequency specified bythe user (20) in the system settings table (140).

Bots are independent components of the application that have specifictasks to perform. In the case of scenario bots, their primary task is toidentify likely scenarios for the evolution of the causal performancedrivers and event risks by organization and organization level. Thescenario bots use information from the element definition table (184),the factor definition table (185) and the event risk table (186) todevelop forecasts for the evolution of causal performance drivers andrisks under normal conditions, extreme conditions and a blendedextreme-normal scenario. Every scenario bot activated in this blockcontains the information shown in Table 30.

TABLE 30 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Type: normal, extreme or blended 6.Organization 7. Organization levelAfter the scenario bots are initialized, they activate in accordancewith the frequency specified by the user (20) in the system settingstable (140). Once activated, they retrieve the required information anddevelop a variety of scenarios as described previously. After thescenario bots complete their calculations, they save the resultingscenarios in the scenario table (187) by organization and organizationlevel in the ContextBase (60) and processing advances to a block 341.

The software in block 341 checks the system settings table (140) in theapplication database (50) to see if knowledge is going to be capturedfrom a subject matter expert. If the current calculations are not goingto utilize knowledge from a subject matter expert (25), then processingadvances to a software block 344. Alternatively, if the currentcalculations are going to utilize knowledge captured from a subjectmatter expert (25), then processing advances to a software block 342.

The software in block 342 will guide the subject matter expert (25)through a series of steps as required to capture knowledge via theknowledge capture window (707). The subject matter expert (25) willprovide knowledge by selecting from a template of pre-defined elements,events, actions and organization structure graphics that are developedfrom the information stored in the ContextBase (60). The subject-matterexpert (25) is first asked to define what type of knowledge will beprovided. The choices will include each of the six context layers aswell as element definition, factor definition, event risk definition andscenarios. On this same screen, the subject-matter expert (25) will alsobe asked to decide whether basic structures or probabilistic structureswill provided in this session, if this session will require the use of atime-line and if the session will include the lower level subjectmatter. The selection regarding type of structures will determine whattype of samples will be displayed on the next screen. If the use of atime-line is indicated, then the user will be prompted to: select areference point—examples would include today, event occurrence, when Istarted, etc.; define the scale being used to separate differenttimes—examples would include seconds, minutes, days, years, etc.; andspecify the number of time slices being specified in this session. Theselection regarding which type of knowledge will be provided determinesthe display for the last selection made on this screen. As shown inTable 31 there is a natural hierarchy to the different types ofknowledge that can be provided by subject-matter experts (25). Forexample, mission level knowledge would be expected in includerelationships with the organization, instant impact, tactical andphysical context layers. If the subject-matter expert (25) agrees, theknowledge capture, window (707) will guide the subject-matter expert(25) to provide knowledge for each of the “lower level” knowledge areasby following the natural hierarchies shown in Table 31.

TABLE 31 Starting point ″Lower level″ knowledge areas MissionOrganization, Instant Impact, Tactical, Physical Organization InstantImpact, Tactical, Physical Instant Impact Tactical, PhysicalSummarizing the preceding discussion, the subject-matter expert (25) hasused the first screen to select one of ten types of knowledge to beprovided (mission, organization, instant impact, tactical, physical,social environment, element, factor, event risk or scenario). Thesubject-matter expert (25) has also chosen to provide this informationin one of four formats: basic structure without timeline, basicstructure with timeline, relational structure without timeline orrelational structure with timeline. Finally, the subject-matter expert(25) has indicated whether or not the session will include an extensionto capture “lower level” knowledge. Each selection made by thesubject-matter expert (25) will be used to identify the combination ofelements, events, actions and organization structure chosen for displayand possible selection. This information will be displayed in a mannerthat is very similar to the manner in which stencils are made availableto Visio® users for use in the workspace.

The next screen displayed by the knowledge capture window (707) will, ofcourse, depend on which combination of knowledge, structure and timelinetypes the subject-matter expert (25) has selected. In addition todisplaying the sample structures and elements to the subject-matterexpert (25), this screen will also provide the subject-matter expert(25) with the option to use graphical operations to change therelationship structures, define new relationships and define newelements. The thesaurus table (142) in the application database providesgraphical operators for: adding an element or factor, consuming anelement, acquiring an element, changing element or factor values, addinga relationship, changing the strength of a relationship, identifying anevent cycle, identifying a random relationship, identifying commitments,identifying constraints and indicating preferences.

The subject-matter expert (25) would be expected to select theorganization structure that most closely resembles the knowledge that isbeing communicated and add it to the workspace in the knowledge capturewindow (707). After adding it to the workspace, the subject-matterexpert (25) will then edit elements and events and add elements, eventsand descriptive information as required to fully describe the knowledgebeing captured from the perspective represented by the screen. Ifrelational information is being specified, then the knowledge capturewindow (707) will give the subject-matter expert (25) the option ofusing graphs, numbers or letter grades to communicate the informationregarding probabilities. If a timeline is being used, then the nextscreen displayed by the knowledge capture window (707) will be thescreen for the same perspective from the next time period in the timeline. The starting point for the next period knowledge capture will bethe final version of the knowledge captured in the prior time period.After completing the knowledge capture for each time period for a givenlevel, the knowledge capture window (707) will guide the subject-matterexpert (25) to the “lower level” areas where the process will berepeated using samples that are appropriate to the context layer or areabeing reviewed. At all steps in the process, the subject matterbackground information in the ContextBase (60) and the knowledgecollected during the session will be used to predict elements, actions,events and organization structures that are likely to be added ormodified in the workspace. These “predictions” will be displayed usingflashing symbols in the workspace. The subject-matter expert (25) willalso be provided with the option of turning the predictive promptingfeature off. After knowledge has been captured for all knowledge areas,the graphical results will be converted to data base entries and storedin the appropriate tables (171, 172, 173, 174, 175, 179, 184, 185, 186or 187) in the ContextBase (60) before processing advances to a softwareblock 344. Data from simulation programs could be added to theContextBase (60) to provide similar information.

The software in block 344 checks the bot date table (141) anddeactivates segmentation bots with creation dates before the currentsystem date. The software in the block then retrieves the informationfrom the system settings table (140), the element definition table (184)and factor definition table (185) and scenario table (187) to initializesegmentation bots for each organization level in accordance with thefrequency specified by the user (20) in the system settings table (140).Bots are independent components of the application that have specifictasks to perform. In the case of segmentation bots, their primary taskis to use the historical and forecast data to segment the performancecontribution of each element, factor, combination and performance driverinto a base value and a variability or risk component. The system of thepresent invention uses wavelet algorithms to segment the performancecontribution into two components although other segmentation algorithmssuch as GARCH could be used to the same effect. Every segmentation botcontains the information shown in Table 32.

TABLE 32 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.Element, factor, or combination 8. Segmentation algorithmAfter the segmentation bots are initialized, the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated the bots retrieve data fromthe element definition table (184) and the factor definition table (185)and then segment the performance contribution of each element, factor orcombination into two segments. The resulting values by period for eachorganization level are then stored in the element definition table (184)and factor definition table (185) before processing advances to asoftware block 345.

The software in block 345 checks the bot date table (141) anddeactivates simulation bots with creation dates before the currentsystem date. The software in block 345 then retrieves the informationfrom the system settings table (140), the element definition table(184), the factor definition table (185), the event risk table (186) andthe scenario table (187) as required to initialize simulation bots inaccordance with the frequency specified by the user (20) in the systemsettings table (140).

Bots are independent components of the application that have specifictasks to perform. In the case of simulation bots, their primary tasksare to run three different types of simulations for the organization byorganization level and to develop an overall summary of the risks tomission measure performance. The simulation bots run probabilisticsimulations of mission measure performance for each organization levelusing: the normal scenario, the extreme scenario and the blendedscenario. They also run an unconstrained genetic algorithm simulationthat evolves to the most negative value possible over the specified timeperiod. In the preferred embodiment, Monte Carlo models are used tocomplete the probabilistic simulation, however other probabilisticsimulation models such as Quasi Monte Carlo can be used to the sameeffect. The models are initialized using the statistics andrelationships derived from the calculations completed in the priorstages of processing to relate mission measure performance to theperformance driver and event risk scenarios. Every simulation botactivated in this block contains the information shown in Table 33.

TABLE 33 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Type: normal, extreme, blended orunconstrained genetic algorithm 6. Mission measure 7. Organization 8.Organization levelAfter the simulation bots are initialized, they activate in accordancewith the frequency specified by the user (20) in the system settingstable (140). Once activated, they retrieve the required information andsimulate mission measure performance by organization and organizationlevel over the time periods specified by the user (20) in the systemsettings table (140). In doing so, the bots will forecast the range ofperformance and risk that can be expected for the specified missionmeasure by organization and organization level within the confidenceinterval defined by the user (20) in the system settings table (140) foreach scenario. The bots also create a summary of the overall risksfacing the organization for the current mission measure using thegeneral format shown in FIG. 10. After the simulation bots completetheir calculations, the resulting forecasts are saved in the scenariotable (187) by organization and organization level and the risk summaryis saved in the mission layer table (175) and the report table (183) inthe ContextBase (60) before processing advances to a software block 346.

The software in block 346 checks the bot date table (141) anddeactivates mission measure bots with creation dates before the currentsystem date. The software in block 346 then retrieves the informationfrom the system settings table (140), the mission layer table (175), theelement definition table (184) and the factor definition table (185) asrequired to initialize bots for each element of performance, externalfactor, combination or performance driver for the mission measure beinganalyzed. Bots are independent components of the application that havespecific tasks to perform. In the case of mission measure bots, theirtask is to determine the contribution of every element of performance,external factor, combination and performance driver to the missionmeasure being analyzed. The relative contribution of each element,external factor, combination and performance driver is determined byusing a series of predictive models to find the best fit relationshipbetween the element of performance vectors, external factor vectors,combination vectors and performance drivers and the mission measure. Thesystem of the present invention uses 12 different types of predictivemodels to identify the best fit relationship: neural network; CART;projection pursuit regression; generalized additive model (GAM); GARCH;MMDR; redundant regression network; boosted Naive Bayes Regression; thesupport vector method; MARS; linear regression; and stepwise regression.The model having the smallest amount of error as measured by applyingthe mean squared error algorithm to the test data is the best fit model.The “relative contribution algorithm” used for completing the analysisvaries with the model that was selected as the “best-fit”. For example,if the “best-fit” model is a neural net model, then the portion of themission measure attributable to each input vector is determined by theformula shown in Table 34.

TABLE 34$\left( {\sum\limits_{k = 1}^{k = m}\; {\sum\limits_{j = 1}^{j = n}\; {I_{jk}X\mspace{11mu} {O_{k}/{\sum\limits_{j = 1}^{j = n}\; I_{ik}}}}}} \right)/{\sum\limits_{k = 1}^{k = m}\; {\sum\limits_{j = 1}^{j = n}\; {I_{jk}X{\; \;}O_{k}}}}$Where I_(jk) = Absolute value of the input weight from input node j tohidden node k O_(k) = Absolute value of output weight from hidden node kM = number of hidden nodes N = number of input nodesAfter completing the best fit calculations, the bots review the lives ofthe elements of performance that impact mission measure performance. Ifone or more of the elements has an expected life that is shorter thanthe forecast time period stored in the system settings, then a separatemodel will be developed to reflect the removal of the impact from theelement(s) that are expiring. The resulting values for relative elementof performance and external factor contributions to mission measureperformance are and saved in the element definition table (184) and thefactor definition table (185) by organization level and organization. Ifthe calculations are related to a commercial business then the value ofeach contribution will be saved. The overall model of mission measureperformance is saved in the mission layer table (175) by organizationlevel and organization. Every mission measure bot contains theinformation shown in Table 35.

TABLE 35 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.Element, factor, combination or performance driver 8. Mission MeasureAfter the mission measure bots are initialized by the software in block346 they activate in accordance with the frequency specified by the user(20) in the system settings table (140). After being activated, the botsretrieve information and complete the analysis of the mission measureperformance. As described previously, the resulting relativecontribution percentages are saved in the element definition table (184)and the factor definition table (185) by organization level andorganization. The overall model of mission measure performance is savedin the mission layer table (175) by organization level and organizationbefore processing advances to a software block 352.

Before continuing the discussion the remaining calculations in thissection it is appropriate to briefly review the processing that has beencompleted in this portion of system (100) processing. At this point, thephysical layer table (171), tactical layer table (172) and instantimpact layer table (173) contain information that defines theadministrative status of the organization by element. The socialenvironment layer table (179) contains information that identifies theexternal factors that affect mission measure performance. As detailedabove, the organization layer table (174) now contains information thatidentifies the inter-relationship between the different elements, risksand factors that drive mission measure performance. The mission layertable (175) now contains a model that identifies the elements andfactors that support mission measure performance by organization leveland organization. The mission layer table (175) also contains a summaryof the event risks and factor risks that threaten mission measureperformance. The event risks include standard event risks, strategicevent risks, contingent liabilities and extreme risks while thevariability risks include both element variability risks and factorvariability risks. In short, the ContextBase (60) now contains acomplete picture of the factors that will determine mission measureperformance for the organization. In the steps that follow, theContextBase (60) will be updated to support the analysis of allorganization mission measure, organizational alignment will beevaluated, the efficient frontier for organization performance will bedefined and the organization ontology will be formalized and stored. Thenext step in this processing is completed in software block 352.

The software in block 352 checks the mission layer table (175) in theContextBase (60) to determine if all mission measures for allorganizations have current models. If all mission measure models are notcurrent, then processing returns to software block 301 and theprocessing described above for this portion (300) of the applicationsoftware. Alternatively, if all mission measure models are current, thenprocessing advances to a software block 354.

The software in block 354 retrieves the previously stored values formission performance from the mission layer table (175) before processingadvances to a software block 355. The software in block 355 checks thebot date table (141) and deactivates measure relevance bots withcreation dates before the current system date. The software in block 355then retrieves the information from the system settings table (140) andthe mission layer table (175) as required to initialize a bot for eachorganization being analyzed. Bots are independent components of theapplication that have specific tasks to perform. In the case of measurerelevance bots, their task is to determine the relevance of each of thedifferent mission measures to mission performance. The relevance of eachmission measure is determined by using a series of predictive models tofind the best fit relationship between the mission measures and missionperformance. The system of the present invention uses 12 different typesof predictive models to identify the best fit relationship: neuralnetwork; CART; projection pursuit regression; generalized additive model(GAM); GARCH; MMDR; redundant regression network; boosted Naive BayesRegression; the support vector method; MARS; linear regression; andstepwise regression. The model having the smallest amount of error asmeasured by applying the mean squared error algorithm to the test datais the best fit model. Bayes models are used to define the probabilityassociated with each relevance measure and the Viterbi algorithm is usedto identify the most likely contribution of all elements, factors andrisks by organization level as required to produce a report in theformat shown in FIG. 11. The relative contribution each of missionmeasure to mission performance is saved in the mission layer table (175)by organization level and organization. Every measure relevance botcontains the information shown in Table 36.

TABLE 36 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Organization level 7.Mission MeasureAfter the measure relevance bots are initialized by the software inblock 355 they activate in accordance with the frequency specified bythe user (20) in the system settings table (140). After being activated,the bots retrieve information and complete the analysis of the missionperformance. As described previously, the relative mission measurecontributions to mission performance and the associated probability aresaved in the mission layer table (175) by organization level andorganization before processing advances to a software block 356.

The software in block 356 retrieves information from the mission measuretable (175) and then checks the mission measures by organization levelto determine if they are in alignment with the overall mission. Asdiscussed previously, lower level measures that are out of alignment canbe identified by the presence of measures from the same level with moreimpact. For example, employee training could be shown to be a strongperformance driver for the organization. If the human resourcesdepartment (that is responsible for both training and performanceevaluations) was using a timely performance evaluation measure, then themeasures would be out of alignment. If mission measures are out ofalignment, then the software in block 356 prompts the manager (21) viathe mission edit data window (708) to change the mission measures byorganization level as required to Alternatively, if mission measures byorganization level are in alignment, then processing advances to asoftware block 357.

The software in block 357 checks the bot date table (141) anddeactivates frontier bots with creation dates before the current systemdate. The software in block 357 then retrieves information from thesystem settings table (140), the element definition table (184), thefactor definition table (185), the event risk table (186) and thescenarios table (187) as required to initialize frontier bots for eachscenario. Bots are independent components of the application that havespecific tasks to perform. In the case of frontier bots, their primarytask is to define the efficient frontier for organization performanceunder each scenario. The top leg of the efficient frontier for eachscenario is defined by successively adding the features, options andperformance drivers that improve while increasing risk to the optimalmix in resource efficiency order. The bottom leg of the efficientfrontier for each scenario is defined by successively adding thefeatures, options and performance drivers that decrease performancewhile decreasing risk to the optimal mix in resource efficiency order.Every frontier bot contains the information shown in Table 37.

TABLE 37 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (date, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization 6. Scenario: normal,extreme and blendedAfter the software in block 357 initializes the frontier bots, theyactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After completing their calculations,the results of all 3 sets of calculations (normal, extreme and mostlikely) are saved in the report table (183) in sufficient detail togenerate a chart like the one shown in FIG. 12 before processingadvances to a software block 358.

The software in block 358 takes the previously stored definitions of keyterms, events, organization levels, context layers, event risks andstores them in the ontology table (182) using the OWL language. Use ofthe rdf based OWL language will enable the synchronization of theorganizations ontology with other organizations and will facilitate theextraction and use of information from the semantic web. After theorganization ontology is saved in the ContextBase(60), processingadvances to a software block 362. The software in block 362 checks thesystem settings table (140) in the application database (50) todetermine if event models will be created. If event models are not goingto be created, then processing advances to a software block 372.Alternatively, if event models are going to be developed, thenprocessing advances to a software block 364. The software in block 364prompts the user (20) via the event selection window (709) to select theevents that will have models developed. Actions are a subset of eventsso they can also be selected for modeling. The events selected formodeling are stored in the event model table (188) in the ContextBase(60) before processing advances to a software block 365. The software inblock 365 retrieves the previously stored event history and forecastinformation from the tactical layer table (172) before processingadvances to a software block 304 where the processing used to identifycausal performance drivers is used to identify causal event drivers.When models for each selected event are stored in the event model table(188) processing advances to software block 372.

The software in block 372 checks the system settings table (140) in theapplication database (50) to determine if impact models will be created.If impact models are not going to be created, then processing advancesto a software block 402. Alternatively, if impact models are going to bedeveloped, then processing advances to a software block 374. Thesoftware in block 374 prompts the user (20) via the impact selectionwindow (710) to select the impacts that will have models developed. Theimpacts selected for modeling are stored in the impact model table (189)in the ContextBase (60) before processing advances to a software block375. The software in block 365 retrieves the previously stored impacthistory and forecast information from the instant impact layer table(173) before processing advances to a software block 304 where theprocessing used to identify causal performance drivers is used toidentify causal impact drivers. When models for each selected impact arestored in the impact model table (189) processing advances to softwareblock 402.

Context Frame Definition

The flow diagram in FIG. 8 details the processing that is completed bythe portion of the application software (400) that generates contextframes and optionally displays and prints management reports detailingthe mission performance of the organization. Processing in this portionof the application starts in software block 402.

The software in block 402 retrieves information from the system settingstable (140), the physical layer table (171), the tactical layer table(172), the instant impact layer table (173), the organization layertable (174), the mission layer table (175), the social environment layertable (179), the element definition table (184), the factor definitiontable (185) and the event risk table (186) as required to define contextframes for every organization level and combination specified by theuser (20) in the system settings table. The resulting frame definitionsare stored in the context frame table (190) before processing advancesto a software block 403.

The software in block 403 prompts the user (20) via the frame definitiondata window (711) to define additional context frames. If the userdefines new context frames, then the information required to define theframe is retrieved from the physical layer table (171), the tacticallayer table (172), the instant impact layer table (173), theorganization layer table (174), the mission layer table (175), thesocial environment layer table (179), the element definition table(184), the factor definition table (185) and/or the event risk table(186) and the context frame specification is stored in the context frametable (190). The context frames developed by the software in block 402will identify and include information regarding all elements that areimpacted by a change in a given context frame. In block 403, the user(20) has the option of limiting the elements included in the frame toinclude only those elements that have a certain level of impact. Forexample, if a change in supply chain operation had a very weak causalimpact on brand strength, then brand information could be excluded fromthe frame specified by the user (20) in this block. If event models orimpact models have been created, then the software in block 403 candefine context frames for event and impact analysis using the sameprocedure described for developing mission measure context frames. Thenewly defined context frames for events, impacts and mission measuresare stored in the context frame table (190) processing passes to asoftware block 404.

The software in block 404 supports the complete context interface datawindow (712). The complete context interface data window (712) is wherethe Complete Context™ Systems (601, 602, 603, 604, 605, 606, 607 and608) request context frames for use in completing their functions. Inaddition to supplying context frames to the standard applications via anetwork (45), the software in block 404 supports integration andtranslation with other ontologies as required to complete transactionsand analysis in automated fashion. The Complete Context™ Systems (601,602, 603, 604, 605, 606, 607 and 608) all have the ability to supportother ontologies as well as the translation and integration of theseontologies with the ontology developed by the system of the presentinvention. The software in block 404 provides context frames to thestandard applications upon request. Processing continues to a softwareblock 410.

The software in block 410 completes two primary functions. First it usesthe narrow system interface data window (713) to interact with eachnarrow system (30) as required identify the context quotient for thatsystem. Second, it provides context frame information to each narrowsystem (30) in a format that can be used by that narrow system (30). Thecontext quotient is a score that is given to each narrow system (30)that identifies the relative ability of the narrow system to flexiblyprocess information from the six different context layers. The scoresrange from 2 to 200 with 200 being the highest score. The CompleteContext™ Systems (601, 602, 603, 604, 605, 606, 607 and 608) all havecontext quotients of 200. Twenty points are given for each context layerthe narrow system is able to process. For example, a supply chainoptimization system with the ability to optimize supplier purchase cost(instant impact) given an inventory status (physical) and order status(tactical) would be given sixty points—twenty points for each of the 3layers it is able to process. If the supply chain optimization systemwas able to change its optimal solution based on new informationregarding the relationship between the supply chain and other elementsof performance (organization) like the customer base and channelpartners, then another twenty points would be given for its ability toprocess organization layer information. The process is repeated for eachlayer. When the narrow system (30) changes its results in response toinput from a new layer, then another twenty points are added to thecontext quotient for that system. Another thirteen points are awardedfor the ability to respond to changes in the relative importance ofdifferent attributes within a context layer. For example, many systemsinclude one or two factors from the social environment in theiranalyses, however, as new factors become important, these systems failto recognize the new factors. The points awarded for each “ability” arenot particularly important, what is important is that the contextquotient score consistently reflects the ability of each system toreliably process the full spectrum information from each of the sixcontext layers in the current environment and in the future when therelative importance of different attributes when each layer are expectedto change. The results of the evaluation of the context quotient for anarrow system (30) seeking data from the system of the present inventionare saved in the context quotient table (192) in the ContextBase (60).The results of the context quotient analysis are used to determine whichcontext layers should be included in the context frame sent to eachnarrow system (30). After defining a context frame for the narrow systemin a manner similar to that described previously for complete contextframes, a packet containing the required information is transmitted tothe narrow system (30) via a network. Alternatively, an operating systemlayer could be propagated as described in cross-referenced patentapplication Ser. Nos. 10/071,164 filed Feb. 7, 2002; 10/124,240 filedApr. 18, 2002 and 10/124,327 filed Apr. 18, 2002. The ability to supportontology translation and integration is not provided in this softwareblock as there are no known narrow systems with the ability to supportthe development and communication of a complete ontology. The ability tosupport this function could easily be added. The software in block 410evaluates context quotients and provides customized context frames tothe narrow systems (30) upon request. Processing continues to a softwareblock 411.

The software in block 411 prompts the user (20) via the report displayand selection data window (714) to review and select reports forprinting. The format of the reports is either graphical, numeric or bothdepending on the type of report the user (20) specified in the systemsettings table (140). If the user (20) selects any reports for printing,then the information regarding the selected reports is saved in thereport table (182). After the user (20) has finished selecting reports,the selected reports are displayed to the user (20) via the reportdisplay and selection data window (714). After the user (20) indicatesthat the review of the reports has been completed, processing advancesto a software block 412. The processing can also pass to block 412 ifthe maximum amount of time to wait for no response specified by the user(20) in the system settings table is exceeded and the user (20) has notresponded.

The software in block 412 checks the report table (182) to determine ifany reports have been designated for printing. If reports have beendesignated for printing, then processing advances to a block 415. Itshould be noted that in addition to standard reports like theperformance risk matrix (FIG. 10), the mission performance matrix (FIG.11), and the graphical depictions of the efficient frontier shown (FIG.12), the system of the present invention can generate reports that rankthe elements, external factors and/or risks in order of their importanceto mission performance and/or mission risk by organization level, bymission measure and/or for the organization as a whole. A format for areport of this type is shown in FIG. 15. The system can also producereports that compare results to plan for actions, impacts and missionmeasure performance if expected performance levels have been specifiedand saved in appropriate context layer as well as “metrics” reports thattrace the historical values for performance drivers over time. Thesoftware in block 415 sends the designated reports to the printer (118).After the reports have been sent to the printer (118), processingadvances to a software block 417. Alternatively, if no reports weredesignated for printing, then processing advances directly from block412 to block 417.

The software in block 417 checks the system settings table (140) todetermine if the system is operating in a continuous run mode. If thesystem is operating in a continuous run mode, then processing returns toblock 205 and the processing described previously is repeated inaccordance with the frequency specified by the user (20) in the systemsettings table (140). Alternatively, if the system is not running incontinuous mode, then the processing advances to a block 418 where thesystem stops.

Thus, the reader will see that the system and method described abovetransforms data and information from disparate narrow systems into aKnowledge Based Performance Management System (100). The level ofdetail, breadth and speed of the analysis gives users of the integratedsystem the ability to manage their operations in an fashion that is lesscomplex and more powerful than any method currently available to usersof the isolated, narrowly focused management systems.

While the above description contains many specificities, these shouldnot be construed as limitations on the scope of the invention, butrather as an exemplification of one preferred embodiment thereof.Accordingly, the scope of the invention should be determined not by theembodiment illustrated, but by the appended claims and their legalequivalents.

1-58. (canceled)
 59. An apparatus, comprising: organization narrowsystems, means for aggregating and analyzing the data from said systemsas required to develop context layers from the group consisting ofphysical, tactical, instant impact, organization, mission, socialenvironment and combinations thereof, and means for providing access tothe data from said systems in accordance with an organization contextwhere the organization context is defined by one or more context layers.60. The apparatus of claim 59 where narrow systems are selected from thegroup consisting of accounting systems, alliance management systems,asset management systems, brand management systems, budgeting/financialplanning systems, business intelligence systems, call managementsystems, cash management systems, channel management systems, commodityrisk management systems, content management systems, contract managementsystems, credit-risk management system, customer relationship managementsystems, data integration systems, demand chain systems, decisionsupport systems, document management systems, email management systems,employee relationship management systems, energy risk managementsystems, executive dashboard systems, expense report processing systems,fleet management systems, fraud management systems, freight managementsystems, human capital management systems, human resource managementsystems, incentive management systems, innovation management systems,insurance management systems, intellectual property management systems,intelligent storage systems, interest rate risk management systems,investor relationship management systems, knowledge management systems,learning management systems, location management systems, maintenancemanagement systems, material requirement planning systems, metricscreation system, online analytical processing systems, ontologymanagement systems, partner relationship management systems, payrollsystems, performance management systems, price optimization systems,private exchanges, process management systems, product life-cyclemanagement systems, project management systems, project portfoliomanagement systems, revenue management systems, risk managementinformation system, risk simulation systems, sales force automationsystems, scorecard systems, sensor grid systems, service managementsystems, six-sigma quality management systems, strategic planningsystems, supply chain systems, supplier relationship management systems,support chain systems, taxonomy development systems, technology chainsystems, unstructured data management systems, visitor (web site)relationship management systems, weather risk management systems,workforce management systems, yield management systems and combinationsthereof.
 61. The apparatus of claim 59 where the mission layeridentifies the net impact of elements, risks and external factors on oneor more mission measures where said measures are selected from the groupconsisting of alliance risk, brand risk, channel risk, content risk,contingent liabilities, customer risk, customer relationship risk,current operation risk, derivative risk, employee risk, employeerelationship risk, energy risk, enterprise risk, external factor risk,event risk, fraud risk, information technology risk, total return,intellectual property risk, investment risk, knowledge risk, marketsentiment risk, market risk, market volatility, organization risk,partnership risk, process risk, production equipment risk, product risk,real option risk, technology risk, vendor risk, vendor relationshiprisk, weather risk, revenue, expense, capital change, alliance return,brand return, channel return, content return, customer return, customerrelationship return, current operation return, derivative return,employee return, employee relationship return, enterprise return,external factor return, event return, information technology return,intellectual property return, investment return, knowledge return,market sentiment return, market return, market volatility, organizationreturn, partnership return, process return, production equipment return,product return, real option return, shareholder return, technologyreturn, vendor return, vendor relationship return, alliance value, brandvalue, channel value, content value, contingent liabilities, customervalue, customer relationship value, current operation value, derivativevalue, employee value, employee relationship value, enterprise value,external factor value, event value, information technology value,intellectual property value, investment value, knowledge value, marketsentiment value, market value, market volatility, organization value,partnership value, process value, production equipment value, productvalue, real option value, technology value, vendor value, vendorrelationship value and combinations thereof.
 62. The apparatus of claim59 that supports the development of context frames where context framesare defined by attributes selected from the group consisting of missionmeasures, context layers, organization levels, organizations andcombinations thereof.
 63. The apparatus of claim 59 where the means forproviding access to the data from narrow systems in accordance with anorganization context further comprises a datamart, a data warehouse, astorage area network or a virtual database.
 64. The apparatus of claim59 where mission measures further comprise any quantifiable measure. 65.The apparatus of claim 59 that defines an organization ontology.
 66. Theapparatus of claim 59 where the context layers are developed by learningfrom the data.
 67. The apparatus of claim 59 where an organizationfurther comprises an organization combination.
 68. The apparatus ofclaim 59 that supports the development and alignment of measures byorganization level in an automated fashion.
 69. A program storage devicereadable by machine, tangibly embodying a program of instructionsexecutable by a machine to perform method steps for performing anorganization management method, the method steps comprising: aggregatingdata from organization narrow systems and learning from said data asrequired to develop one or more contexts for organization performancewhere said contexts are defined by context layers selected from thegroup consisting of physical, tactical, instant impact, organization,mission, social environment and combinations thereof.
 70. The programstorage device of claim 69 where the method steps further comprise:defining one or more subsets of the organization for separate managementusing at least a portion of said context, creating a context frame forthe one or more subsets of the organization, and managing theperformance of each of the one or more organization subsets using saidcontext frame and one or more standard applications.
 71. The programstorage device of claim 69 where narrow systems are selected from thegroup consisting of accounting systems, alliance management systems,asset management systems, brand management systems, budgeting/financialplanning systems, business intelligence systems, call managementsystems, cash management systems, channel management systems, commodityrisk management systems, content management systems, contract managementsystems, credit-risk management system, customer relationship managementsystems, data integration systems, demand chain systems, decisionsupport systems, document management systems, email management systems,employee relationship management systems, energy risk managementsystems, executive dashboard systems, expense report processing systems,fleet management systems, fraud management systems, freight managementsystems, human capital management systems, human resource managementsystems, incentive management systems, innovation management systems,insurance management systems, intellectual property management systems,intelligent storage systems, interest rate risk management systems,investor relationship management systems, knowledge management systems,learning management systems, location management systems, maintenancemanagement systems, material requirement planning systems, metricscreation system, online analytical processing systems, ontologymanagement systems, partner relationship management systems, payrollsystems, performance management systems, price optimization systems,private exchanges, process management systems, product life-cyclemanagement systems, project management systems, project portfoliomanagement systems, revenue management systems, risk managementinformation system, risk simulation systems, sales force automationsystems, scorecard systems, sensor grid systems, service managementsystems, six-sigma quality management systems, strategic planningsystems, supply chain systems, supplier relationship management systems,support chain systems, taxonomy development systems, technology chainsystems, unstructured data management systems, visitor (web site)relationship management systems, weather risk management systems,workforce management systems, yield management systems and combinationsthereof.
 72. The program storage device of claim 69 where the methodsteps further comprise: storing organization related data in accordancewith one or more organization contexts by context layer.
 73. The programstorage device of claim 72 where the means for storing data inaccordance with an organization context further comprises a datamart, adata warehouse, a storage area network or a virtual database.
 74. Theprogram storage device of claim 69 where the method steps furthercomprise: developing and aligning measures by organization level in anautomated fashion.
 75. The program storage device of claim 69 wherecontext frames are defined by attributes selected from the groupconsisting of mission measures, context layers, organization levels,organizations and combinations thereof.
 76. The program storage deviceof claim 69 where the standard applications support organizationmanagement activities selected from the group consisting of analysis,forecast, optimization, planning, project management, review,transaction and combinations thereof.
 77. A data method, comprising:establishing an interface with a narrow system; testing the ability ofsaid narrow system to process different types of data from differentcombinations of context layers using said interface; and tailoring thedata provided to said narrow systems based on the results of said test.78. The method of claim 77 that further comprises assigning a score toeach narrow system based on the test results.
 79. The method of claim 77that further comprises assigning points for the ability to process datafrom each of one or more context layers.
 80. The method of claim 77 thatfurther comprises assigning points for the ability to change the type ofdata processed from a given context layer.
 81. The method of claim 77where narrow systems are selected from the group consisting ofaccounting systems, alliance management systems, asset managementsystems, brand management systems, budgeting/financial planning systems,business intelligence systems, call management systems, cash managementsystems, channel management systems, commodity risk management systems,content management systems, contract management systems, credit-riskmanagement system, customer relationship management systems, dataintegration systems, demand chain systems, decision support systems,document management systems, email management systems, employeerelationship management systems, energy risk management systems,executive dashboard systems, expense report processing systems, fleetmanagement systems, fraud management systems, freight managementsystems, human capital management systems, human resource managementsystems, incentive management systems, innovation management systems,insurance management systems, intellectual property management systems,intelligent storage systems, interest rate risk management systems,investor relationship management systems, knowledge management systems,learning management systems, location management systems, maintenancemanagement systems, material requirement planning systems, metricscreation system, online analytical processing systems, ontologymanagement systems, partner relationship management systems, payrollsystems, performance management systems, price optimization systems,private exchanges, process management systems, product life-cyclemanagement systems, project management systems, project portfoliomanagement systems, revenue management systems, risk managementinformation system, risk simulation systems, sales force automationsystems, scorecard systems, sensor grid systems, service managementsystems, six-sigma quality management systems, strategic planningsystems, supply chain systems, supplier relationship management systems,support chain systems, taxonomy development systems, technology chainsystems, unstructured data management systems, visitor (web site)relationship management systems, weather risk management systems,workforce management systems, yield management systems and combinationsthereof.
 82. The method of claim 77 where the context layers areselected from the group consisting of physical, tactical, instantimpact, organization, mission, social environment and combinationsthereof.
 83. An architecture for use in designing, deploying, andmanaging a plurality of applications on a distributed computing systemfor an organization, the architecture comprising: a plurality of layerswhere at least one layer is an application layer where a single set ofcommon applications resident in the application layer manage andoptimize a performance of any subset of an organization for a completecontext, where said complete context for an organization subsetcomprises the context of claim 105 with one or more quantitativemeasures for evaluating performance, and where the common applicationsincorporate an analysis of a network effect of a plurality of risks andelements of value on one or more performance measures and are selectedfrom the group consisting of analysis, forecast, optimization, planning,project management, review, transaction and combinations thereof. 84.The software architecture of claim 83 where a quantitative measures isselected from the group consisting of alliance risk, brand risk, channelrisk, content risk, contingent liabilities, customer risk, customerrelationship risk, current operation risk, derivative risk, employeerisk, employee relationship risk, energy risk, enterprise risk, externalfactor risk, event risk, fraud risk, information technology risk, totalreturn, intellectual property risk, investment risk, knowledge risk,market sentiment risk, market risk, market volatility, organizationrisk, partnership risk, process risk, production equipment risk, productrisk, real option risk, technology risk, vendor risk, vendorrelationship risk, weather risk, revenue, expense, capital change,alliance return, brand return, channel return, content return, customerreturn, customer relationship return, current operation return,derivative return, employee return, employee relationship return,enterprise return, external factor return, event return, informationtechnology return, intellectual property return, investment return,knowledge return, market sentiment return, market return, marketvolatility, organization return, partnership return, process return,production equipment return, product return, real option return,shareholder return, technology return, vendor return, vendorrelationship return, alliance value, brand value, channel value, contentvalue, contingent liabilities, customer value, customer relationshipvalue, current operation value, derivative value, employee value,employee relationship value, enterprise value, external factor value,event value, information technology value, intellectual property value,investment value, knowledge value, market sentiment value, market value,market volatility, organization value, partnership value, process value,production equipment value, product value, real option value, technologyvalue, vendor value, vendor relationship value and combinations thereof.85. The software architecture of claim 83 where a subset of theorganization is defined by attributes selected from the group consistingof context layers, organization levels, organizations and combinationsthereof.
 86. The software architecture of claim 83 where a commonapplication within a set of common applications is selected from thegroup consisting of an application for analyzing the impact of userspecified changes on a defined organization subset, an application forforecasting the value of a specified variable using context layer datafor a specified defined organization subset, an application forsimulating organization performance and identifying the optimal mode foroperating a defined organization subset for one or more measures, anapplication for establishing expected levels and priorities for actions,events and measures for a defined organization subset, an applicationfor developing action, element, impact, measure performance and riskreports in standard formats for a defined organization subset, anapplication that analyzes the impact of a project or a group of projectson a defined organization subset and determines the feature set thatwill optimize the impact of the project or group of projects on saiddefined organization subset and combinations thereof.
 87. A data method,comprising: integrating organization data in accordance with anorganization context model, and using said model to develop anorganization ontology that supports the management and optimization ofperformance for one or more quantitative measures for any subset of theorganization using a set of common applications.
 88. The method of claim87 where the ontology is developed by learning from the data.
 89. Themodel of claim 87 where organization context further comprises one ormore context layers where the layers are selected from the groupconsisting of physical, tactical, instant impact, organization, mission,social environment and combinations thereof.
 90. The model of claim 87where the ingredients driving quantitative measure performance areselected from the group consisting of elements of performance, projects,resources, risks, social environment factors and combinations thereof.91. The model of claim 87 where elements of performance are selectedfrom the group consisting of alliances, brands, capital, channels,customers, customer relationships, employees, employee relationships,human capital, intellectual property, inventory, investors, investorrelationships, knowledge, partners, partner relationships, processes,products, suppliers, supplier relationships, support chains, visitors,the workforce, workforce time and combinations thereof.
 92. The model ofclaim 87 where social environment factors are selected from the groupconsisting of numerical indicators of conditions external to theorganization, numerical indications of prices external to theorganization, numerical indications of organization conditions comparedto external expectations of organization condition, numericalindications of the organization performance compared to externalexpectations of organization performance and combinations thereof. 93.The model of claim 87 where risks are selected from the group consistingof factor variability risks, element variability risks, base marketrisk, industry market risk, market volatility, strategic event risks,extreme event risks, event risks, contingent liabilities andcombinations thereof.
 94. The model of claim 87 that supports developingand aligning measures by organization level in an automated fashion. 95.A database that provides access to organization related data by contextlayer in accordance with one or more organization contexts.
 96. Thedatabase of claim 95 that further comprises a datamart, a datawarehouse, a storage area network or a virtual database.
 97. Thedatabase of claim 95 where the context layers are selected from thegroup consisting of physical, tactical, instant impact, organization,mission, social environment and combinations thereof.
 98. The databaseof claim 95 where the one or more organization contexts are defined bycontext frames.
 99. An architecture for use in designing, deploying, andmanaging a plurality of applications on a distributed computing systemfor an organization, the architecture comprising: a plurality of layerswhere at least one layer is an application layer where a single set ofcommon applications resident in the application layer manage aperformance of any subset of the organization for a complete context,where the architecture enables an optimization of any subset of theorganization for a complete context, where said complete context for anorganization subset comprises the context from claim 111 with one ormore quantitative measures for evaluating performance, and where thecommon applications incorporate an analysis of the network effect of aplurality of risks, external factors and elements of value on one ormore performance measures and are selected from the group consisting ofanalysis, forecast, optimization, planning, project management, review,transaction and combinations thereof.
 100. The software architecture ofclaim 99 where a quantitative measure is selected from the groupconsisting of alliance risk, brand risk, channel risk, content risk,contingent liabilities, customer risk, customer relationship risk,current operation risk, derivative risk, employee risk, employeerelationship risk, energy risk, enterprise risk, external factor risk,event risk, fraud risk, information technology risk, total return,intellectual property risk, investment risk, knowledge risk, marketsentiment risk, market risk, market volatility, organization risk,partnership risk, process risk, production equipment risk, product risk,real option risk, technology risk, vendor risk, vendor relationshiprisk, weather risk, revenue, expense, capital change, alliance return,brand return, channel return, content return, customer return, customerrelationship return, current operation return, derivative return,employee return, employee relationship return, enterprise return,external factor return, event return, information technology return,intellectual property return, investment return, knowledge return,market sentiment return, market return, market volatility, organizationreturn, partnership return, process return, production equipment return,product return, real option return, shareholder return, technologyreturn, vendor return, vendor relationship return, alliance value, brandvalue, channel value, content value, contingent liabilities, customervalue, customer relationship value, current operation value, derivativevalue, employee value, employee relationship value, enterprise value,external factor value, event value, information technology value,intellectual property value, investment value, knowledge value, marketsentiment value, market value, market volatility, organization value,partnership value, process value, production equipment value, productvalue, real option value, technology value, vendor value, vendorrelationship value and combinations thereof.
 101. The softwarearchitecture of claim 99, wherein a subset of the organization isdefined by attributes selected from the group consisting of contextlayers, organization levels, organizations and combinations thereof.102. The software architecture of claim 99, wherein a common applicationwithin a set of common applications is selected from the groupconsisting of an application for analyzing the impact of user specifiedchanges on a defined organization subset, an application for forecastingthe value of a specified variable using context layer data for aspecified defined organization subset, an application for simulatingorganization performance and identifying the optimal mode for operatinga defined organization subset for one or more measures, an applicationfor establishing expected levels and priorities for actions, events andmeasures for a defined organization subset, an application fordeveloping action, element, impact, measure performance and risk reportsin standard formats for a defined organization subset, an applicationthat analyzes the impact of a project or a group of projects on adefined organization subset and determines the feature set that willoptimize the impact of the project or group of projects on said definedorganization subset and combinations thereof.
 103. An architecture forcontext aware computing, the architecture comprising: a data accesslayer operable to exchange data with a plurality of systems and topresent the data to a context modeling layer through a uniforminterface; a context modeling layer operable to develop a context fromthe data obtained from the data object access layer and to present thecontext to a frame layer through a uniform interface; a frame layeroperable to obtain a perspective from a user interface and to develop acontext frame which provides a context for the perspective to anapplication in a uniform format, and an application layer that containsone or more applications that use said context frame to complete one ormore useful context-aware functions.
 104. The architecture of claim 103,wherein one or more applications are selected from the group consistingof analysis, forecast, optimization, planning, project management,review, transaction and combinations thereof.
 105. A computer programproduct embodied on a computer readable medium and comprising programcode for directing at least one computer to complete processing inaccordance with an architecture for context aware computing, comprising:a data access layer operable to exchange data with a plurality ofsystems and to present the data to a context modeling layer through auniform interface; a context modeling layer operable to develop acontext from the data obtained from the data access layer and to presentthe context to a frame layer through a uniform interface; a frame layeroperable to obtain a perspective from a user interface and to develop acontext frame which provides a context for the perspective to aplurality of applications in a uniform format, and an application layerthat contains one or more applications that use said context frame tocomplete one or more useful context-aware functions where a contextfurther comprises a complete context that contains information thatdefines an impact of changes in operation on a long term performancemeasure, information that defines a social environment context andinformation selected from the group consisting of information thatdefines a tactical context, information that defines a physical context,information that defines a measure context and combinations thereof.106. The computer program product of claim 105, wherein one or moreapplications are selected from the group consisting of analysis,forecast, optimization, planning, project management, review,transaction and combinations thereof.
 107. The computer program productof claim 105, wherein a plurality of systems are selected from the groupconsisting of accounting systems, alliance management systems, assetmanagement systems, brand management systems, budgeting/financialplanning systems, business intelligence systems, call managementsystems, cash management systems, channel management systems, commodityrisk management systems, content management systems, contract managementsystems, credit-risk management system, customer relationship managementsystems, data integration systems, demand chain systems, decisionsupport systems, document management systems, email management systems,employee relationship management systems, energy risk managementsystems, executive dashboard systems, expense report processing systems,fleet management systems, fraud management systems, freight managementsystems, human capital management systems, human resource managementsystems, incentive management systems, innovation management systems,insurance management systems, intellectual property management systems,intelligent storage systems, interest rate risk management systems,investor relationship management systems, knowledge management systems,learning management systems, location management systems, maintenancemanagement systems, material requirement planning systems, metricscreation system, online analytical processing systems, ontologymanagement systems, partner relationship management systems, payrollsystems, performance management systems, price optimization systems,private exchanges, process management systems, product life-cyclemanagement systems, project management systems, project portfoliomanagement systems, revenue management systems, risk managementinformation system, risk simulation systems, sales force automationsystems, scorecard systems, sensor grid systems, service managementsystems, six-sigma quality management systems, strategic planningsystems, supply chain systems, supplier relationship management systems,support chain systems, taxonomy development systems, technology chainsystems, unstructured data management systems, visitor (web site)relationship management systems, weather risk management systems,workforce management systems, yield management systems and combinationsthereof.
 108. The computer program product of claim 105 that furthercomprises a plurality of intelligent agents.
 109. An architecture forcontext aware applications, comprising an application layer with: a dataaccess function operable to exchange data with a plurality of systemsand to present the data to a context modeling function through a uniforminterface; a context modeling function operable to develop a contextfrom the data obtained from the data access function and to present thecontext to a frame function through a uniform interface; a framefunction operable to obtain a perspective from a user interface and todevelop a context frame which provides a context for the perspective toa plurality of application in a uniform format, and one or moreapplications that uses said context frame to complete one or more usefulcontext-aware functions.
 110. The application layer of claim 109,wherein one or more applications are selected from the group consistingof analysis, forecast, optimization, planning, project management,review, transaction and combinations thereof.
 111. A computer programproduct embodied on a computer readable medium and comprising programcode for directing at least one computer to complete processing inaccordance with an architecture for context aware computing, comprisingan application layer with: a data access function operable to exchangedata with a plurality of systems and to present the data to a contextmodeling function through a uniform interface; a context modelingfunction operable to develop a context from the data obtained from thedata access layer and to present the context to a frame function througha uniform interface; a frame function operable to obtain a perspectivefrom a user interface and to develop a context frame which provides acontext for the perspective to a plurality of applications in a uniformformat, and one or more applications that use said context frame tocomplete one or more useful context-aware functions. where a contextfurther comprises a complete context that contains information thatdefines an impact of changes in operation on a short term performancemeasure, information that defines a social environment context andinformation selected from the group consisting of information thatdefines a tactical context, information that defines a physical context,information that defines a measure context and combinations thereof.112. The computer program product of claim 111, wherein one or moreapplications are selected from the group consisting of analysis,forecast, optimization, planning, project management, review,transaction and combinations thereof.